In-Situ Monitoring and Analysis of the Pitting Corrosion of Carbon Steel by Acoustic Emission

applied sciences

Article In-Situ Monitoring and Analysis of the Pitting Corrosion of Carbon Steel by Acoustic Emission

Junlei Tang 1 , Junyang Li 2, Hu Wang 2,*, Yingying Wang 1 and Geng Chen 3

1 School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China; [email protected] (J.T.); [email protected] (Y.W.) 2 School of Material Science and Engineering, Southwest Petroleum University, Chengdu 610500, China; [email protected] 3 CNOOC Energy Technology & Services-Shanghai Environmental Engineering & Technology Branch, Shanghai 200335, China; [email protected] * Correspondence: [email protected]; Tel.: +86-028-83037361

Received: 5 November 2018; Accepted: 8 February 2019; Published: 18 February 2019

Abstract: The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO3 + NaCl solutions. The open circuit potential (OCP) measurement and corrosion morphology in-situ capturing using an optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with a scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After completion of the signal processing, including pre-treatment, shape preserving interpolation, and denoising, for raw AE waveforms, three types of AE signals were classiﬁed in the correlation diagrams of the new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.

Keywords: carbon steel; pitting corrosion; acoustic emission; wavelets; pattern recognition

1. Introduction In the modern construction industry, carbon steel is widely used because of its low price and good mechanical properties. However, its corrosion resistance in an aqueous environment is very low. Especially in the presence of a saline medium, the probability of corrosion increases greatly, mostly including localized corrosion. Structural damage and mechanical performance degradation can easily happen during long term service with localized corrosion. As one of the most destructive forms of localized corrosion, pitting corrosion of carbon steel intensively occurs in many sites, such as steel in reinforced concrete, and steel with degraded coatings of a bridge or water containment structure. Many investigations [1–3] have been done for the detection of structural failure of concrete with the acoustic emission (AE) technique. As the initiation stage of many structural degradation cases, the pitting corrosion of carbon steel needs to be monitored. It is commonly believed that pitting corrosion happens on passivated metals and alloys in a solution of halide ions [4]. The properties of the passive film plays an important role in the initiation, growth, and re-passivation of pits. In most occasions, the pitting process is regarded as containing several stages: (1) Local breakdown of the passive film, represented as the nucleation process, (2) propagation (accelerated corrosion), (3) stable growth, and (possibly) (4) re-passivation. Stainless steels are typically passive initially. The initiation of pitting is caused by the adsorption of halide ions through the passive film. Additionally, the growth of the pit occurs mainly at the bottom as the anodic dissolution inside the pit. The cathodic reaction is nevertheless outside the pit,

Appl. Sci. 2019, 9, 706; doi:10.3390/app9040706 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 706 2 of 19 on inclusions or other defects. Stainless steels are relatively highly resistant to pit initiation, which can be ascribed to the excellent passivity of the γ-Fe2O3 plus Cr2O3 oxide film at the surface. Hence, only a limited amount of pits can be formed on the stainless steel surface. However, when the pit forms, it often grows rapidly and develops in depth. The aggregation of corrosion products at the pit mouth accelerates the corrosion attack beneath the occluded area. The rupture or breakdown of the cover at the pit mouth, including the corrosion products and passive film, may result in re-passivation, which implies the end of the pitting corrosion. As for other materials with a less pronounced passive film, such as carbon steels, pitting corrosion may start at inconsistent pores of the passive film or corrosion products [5]. Different from stainless steels, the growth of pitting undergoes at a relatively slow rate. Pits may repeatedly experience the initiation—growth—re-passivation process [6]. In many occasions, the corrosion attack consists of many shallow pits in the nearby area. The re-passivation of the pit is most probably caused by the passivator in the solution, not by the metal itself. The rupture or breakdown of the passive film and product film is not as notable as stainless steel. It is generally assumed that the iron oxide and ferrous bicarbonate can form as the passive film of carbon steel in a carbonate solution [5,6]. The origin of the passive film is from the oxidation of iron by dissolved oxygen and the deposition of insoluble ferrous bicarbonate. Many studies have focused on the structure and composition of the passive film. Although there are two models of the passive film (the γ-Fe2O3 layer and the γ-Fe2O3 plus Fe3O4 double layer), it is not surprising that the passive film on iron mainly consists of γ-Fe2O3. It is also more acceptable that the passive film of carbon steel in a bicarbonate solution consists of an inner Fe3O4 layer and an outer γ-Fe2O3 layer, both combined with insoluble ferrous bicarbonate [5]. The inhomogeneity of the passive film on carbon steel is the main reason for the initiation of pitting corrosion. Many techniques have been used to study pitting corrosion. The American Society for Testing and Materials (ASTM) has standardized methods for the study/evaluation of pitting corrosion and pitting tests in 6% FeCl3. Electrochemical measurements are most commonly used in investigating pitting corrosion behavior, e.g., polarization curves measuring the break potential of pitting, Eb; protective potential of pitting, Ep; and the free corrosion potential, Ecorr. Current fluctuation behavior in potentiodynamic or potentiostatic polarization is extensively investigated to probe metastable pitting, which is widely regarded as an important phenomenon before stable pitting and can be used to predict stable pitting events. Electrochemical impedance spectroscopy (EIS) is also widely used in the evaluation of the properties of the passive film under pitting attack. Moreover, electrochemical noise (EN) is another useful technique in the investigation of pitting events and the monitoring of pitting corrosion. Although many approaches have been utilized in corrosion investigation, only limited techniques, like EN, linear polarization resistance probe [7,8], electrical resistance probe [9], and the field signature technique [10], can be applied in pitting monitoring. All of them, however, have their limitations. For example, EN is quite sensitive in in-situ recording and identifying the initiation of pitting events [11,12]. However, it is very difficult to interpret the data of the propagation process. Therefore, in-situ monitoring and the prediction of pitting corrosion events in service is still a great challenge in industrial applications. Acoustic emission (AE), an important technique that can be used in corrosion monitoring, has attracted great attention in recent years. In most occasions, the corrosion of metals and alloys is always accompanied by a rapid release of energy in the form of a transient elastic wave. The nature of corrosion monitoring by AE is firstly composed of the relationship between the corrosion attack and the transient elastic wave. Such a relationship is then used for in-situ monitoring of the corrosion attack of the material in service. AE is a non-destructive technique, which aims at in-situ monitoring of corrosion attack by detecting, recording, and analyzing the acoustic emission signal. The modern AE technique began with the work of Kaiser in Germany in the 1950s. In recent years, AE has been widely used in many applications, such as the petroleum and natural gas industry, aerospace industry, transportation industry, and construction industry, etc. [13–16]. Appl. Sci. 2019, 9, 706 3 of 19

Many types of corrosion have been studied by AE [17–25], such as stress corrosion cracking [18–20], abrasion or erosion corrosion [21], and pitting corrosion [22–25]. Mazille [22] proved that AE signals can be easily detected in pitting corrosion. Moreover, a good correlation between AE activity and the pitting rate was observed. Then, the initiation and propagation of pitting corrosion on stainless steels was studied with AE [23]. It further demonstrated that the initiation step of pitting corrosion was not signiﬁcantly emissive, whereas the propagation step was characterized by the emission of resonant signals. They ascribed the signals in the propagation step to the evolution of hydrogen bubbles. More recent studies by other researchers, such as Darowicki, used AE and potentiodynamic methods to investigate the mechanism of pitting corrosion of stainless steel [24]. Wu and Jung used AE to monitor the pitting corrosion on vertically positioned 304 stainless steel, and analyzed the acoustic emission energy parameter [25]. However, most researchers used some methods, such as potentiodynamic polarization, potentiostatic polarization, or heating, to accelerate the pitting corrosion of metals in a short time, thus not realistically simulating the corrosion process of materials under natural conditions. It is very important that the corrosion behavior of steel through in-situ monitoring of the pitting corrosion process under natural conditions is understood to implement structural integrity management for steel construction. The processing and analyzing of the AE signal is the most important step in AE studies. In many studies, the acoustic emission signals are processed and classiﬁed by various methods, like K-means, random forest, wavelet analysis, or some other parameter analysis methods [26–33]. The application of some methods, for instance, the K-means and random forest, only utilize some parameters of the AE waveform (raw or pre-treated). The single use of a wavelet can only optimize AE waveforms. Therefore, there remains much work to do for the development of an effective and integrative method to identify the corrosion type based on raw data from in-situ AE monitoring. In this paper, the pitting corrosion of carbon steel was monitored by the AE technique in NaHCO3 + NaCl solutions, in which the pitting initiation—growth—re-passivation process was present [5]. Open circuit potential experiments were carried out simultaneously to probe the relationship between the AE and electrochemical behavior. In addition, an optical microscope was used to obtain in-situ recordings of the surface morphology of carbon steel under corrosion attack in different conditions and SEM was used to observe the surface micromorphology after corrosion. Finally, Matlab was involved to establish the methods for AE data processing [33] and analyzing.

2. Materials and Methods

2.1. Material Preparation Q235 carbon steel was used for the measurements and the chemical composition of the specimens is listed in Table1. The specimens were cut out from a cold rolled sheet of 2 mm thick steel before measurements and coated with a high-temperature resistant silicone gasket to keep the exposed surface as 15 mm × 15 mm. The specimens were abraded gradually from 180 to 1000 grit silicon carbide paper. Then, they were rinsed with deionized water and acetone, and were dried and stored before use.

Table 1. Composition of Q235 carbon steel.

C Mn Si S P ≤0.22% 0.3%–0.65% ≤0.35% ≤0.05% ≤0.045%

2.2. Corrosion Conditions

The corrosion solution consisted of 2000 mg/L NaHCO3 in the presence of different concentrations of NaCl, ranging from 500 mg/L to 1200 mg/L. The pH value and temperature used in the corrosion experiments was 6.7 and 25 ◦C, respectively. Appl. Sci. 2019, 9, 706 4 of 19

2.3. Setup of Acoustic Emission Monitoring The AE acquisition system and pitting assembly are shown in Figure1. The AE acquisition system consistedAppl. Sci. of 2018 a Mistras, 8, x FOR USB PEER AE REVIEW Node, sensor R15α (50–400 kHz). The sensor was assembled by spring4 of 20 applying 6 N of force. The high vacuum grease was used between the interface of the sensor and the by spring applying 6 N of force. The high vacuum grease was used between the interface of the sensor specimen to ensure the best connection. The AE acquisition options are shown in Table2. and the specimen to ensure the best connection. The AE acquisition options are shown in Table 2.

Figure 1. Schematic diagram of the acoustic emission (AE) monitoring of pitting corrosion: (a) Q235 Figure 1. Schematic diagram of the acoustic emission (AE) monitoring of pitting corrosion: (a) Q235 specimen (dimension in mm), (b) pitting assembly, and the AE system and potential measurement. specimen (dimension in mm), (b) pitting assembly, and the AE system and potential measurement.

Table 2. AE acquisition options. Table 2. AE acquisition options. Threshold PDT HDT HLT Analog Filter Sample Rate Pre-Trigger Length Threshold PDT HDT HLT Analog Filter Sample Rate Pre-Trigger Length 27 dB 200 µs 400 µs 200 µs 100 K–400 KHz 2 MPS 40 µs 2 K 27 dB 200 µs 400 µs 200 µs 100 K–400 KHz 2 MPS 40 µs 2 K 2.4. Electrochemical Measurements and Corrosion Morphology Observations 2.4. Electrochemical Measurements and Corrosion Morphology Observations Open circuit potential (OCP) was measured by a CHI4 electrochemical workstation of Wuhan CorrtestOpen Instruments circuit potential Corp. LTD (OCP) in China. was measured Specimens by of a Q235CHI4 carbonelectrochemical steel were workstation used as the of work Wuhan electrodes.Corrtest AInstruments saturated calomel Corp. LTD electrode in China. (SCE) Specimen was useds of as Q235 the reference carbon steel electrode. were used The corrosion as the work morphologieselectrodes. wereA saturated recorded calomel by an industrialelectrode digital(SCE) was camera used (Yilong as the Electronicreference Technologyelectrode. The Co., corrosion Ltd., Shenzhen,morphologies China, were 5 mega-pixel) recorded by during an industrial the experiment digital camera and a scanning(Yilong Electronic electron microscopeTechnology (SEM,Co., Ltd., ZEISS,Shenzhen, EVO, MA China, 15) after5 mega-pixel) the experiments. during the The experi OCPment and AEand measurements a scanning electron were carried microscope out and (SEM, recordedZEISS, simultaneously. EVO, MA 15) after the experiments. The OCP and AE measurements were carried out and recorded simultaneously. 3. Results 3. Results 3.1. AE and OCP Monitoring 3.1.Figure AE and2 shows OCP Monitoring the OCP and AE monitoring of the corrosion process of Q235 carbon steel in 2000 mg/LFigure NaHCO 2 shows3 in the the OCP presence and AE of different monitoring concentrations of the corrosion of NaCl. process In Figureof Q2352a, carbon OCP behavedsteel in 2000 differently with time in various NaCl concentrations. The OCP shifted slightly towards the positive mg/L NaHCO3 in the presence of different concentrations of NaCl. In Figure 2a, OCP behaved directiondifferently and graduallywith time in stabilized various afterNaCl 50,000concentratio s of immersionns. The OCP in theshifted presence slightly of towards 500 mg/L the NaCl, positive indicatingdirection that and the gradually interface condition stabilized was after inﬂuenced 50,000 s byof theimmersion presence in of NaCl.the presence With the of increase 500 mg/L of theNaCl, NaClindicating concentration, that the the interface OCP behavior condition changed. was influenc In 800ed mg/L by the NaCl, presence the OCP of NaCl. shifted With negatively the increase and of couldthe not NaCl reach concentration, a stable state the in approximatelyOCP behavior 90,000changed. s immersion In 800 mg/L time. NaCl, At the the NaCl OCP concentration shifted negatively of 1000and mg/L could NaCl not and reach 1200 mg/L,a stable the state OCP movedin approximately sharply towards 90,000 the s negative immersion direction time. and At reachedthe NaCl a relativeconcentration stable state of 1000 at 65,000 mg/L s NaCl and 30,000 and 1200 s, respectively. mg/L, the TheOCP above moved results sharply revealed towards the the different negative direction and reached a relative stable state at 65,000 s and 30,000 s, respectively. The above results revealed the different interface conditions of carbon steel in NaHCO3 solution in the presence of different concentrations of NaCl. It was commonly believed that the iron oxide and ferrous bicarbonate form as the passive film of carbon steel in bicarbonate solutions [5,6]. The addition of chloride iron accelerated the breakdown of the passive film and triggered the initiation of pitting.

Appl. Sci. 2019, 9, 706 5 of 19

interface conditions of carbon steel in NaHCO3 solution in the presence of different concentrations of NaCl. It was commonly believed that the iron oxide and ferrous bicarbonate form as the passive film of carbon steel in bicarbonate solutions [5,6]. The addition of chloride iron accelerated the breakdown of the passive filmAppl. andSci. 2018 triggered, 8, x FOR PEER REVIEW the initiation of pitting. Thus, the higher the concentration5 of 20 of Cl−, the faster the passiveThus, the film higher is brokenthe concentration and the of Cl OCP−, the isfaster stabilized. the passive film is broken and the OCP is stabilized.

(a)

V ⋅ 15000 500 mg/ L NaCl 1000 mg/ L NaCl 1200 mg/ L NaCl 800 mg/ L NaCl (b) 12000

9000

6000

3000

0 Cumulative absolute energy/ ms energy/ absolute Cumulative 0 20000 40000 60000 80000 100000 Test time/ s

Figure 2. The openFigure circuit 2. The open potential circuit potential (OCP) (OCP) and and AE AE monitoring monitoring of the of pitting the pittingcorrosion of corrosion carbon steel of carbon steel in in NaCl + NaHCO3 solutions: (a) OCP and cumulative hits versus time, (b) cumulative absolute NaCl + NaHCO3 solutions: (a) OCP and cumulative hits versus time, (b) cumulative absolute energy energy versus time. versus time. Figure 2a also presents the influence of the NaCl concentration on cumulative hits of acoustic Figure2a alsoemission presents and the correlation the influence between the of OCP the and NaCl cumulative concentration hits. In the concentration on cumulative of 500 mg/L, hits of acoustic the cumulative hits remained at very low level during the first 5000 s and then started to increase emission and theslightly, correlation indicating between the corrosion the events OCP occurring and such cumulative Cl− concentrations hits. were In thenot very concentration noticeable. of 500 mg/L, the cumulative hitsThe few remained cumulative hits at veryin the AE low monitoring level duringis in accordance the firstwith the 5000 relatively s and small then variation started to increase range of the OCP. − slightly, indicating theThe cumulative corrosion hits eventscontinuously occurring increased wi suchth time Cl in theconcentrations presence of 2000 mg/L were NaHCO not3 + very noticeable. The few cumulative800 mg/L hits NaCl, in the which AE represented monitoring the occurrence is in accordance of corrosion events with all the time. relatively Repeating smallmetal variation range dissolution beneath the cover of the pits (occluded cell) and the initiation of new pits resulted in the of the OCP. The cumulative hits continuously increased with time in the presence of 2000 mg/L NaHCO3 + 800 mg/L NaCl, which represented the occurrence of corrosion events all the time. Repeating metal dissolution beneath the cover of the pits (occluded cell) and the initiation of new pits resulted in the continuous [6] increasing of cumulative hits. Most likely, the breakdown of the corrosion product film upon the occlude cell induced the sudden ascent of the cumulative hits at about 80,000 s because this process could be highly emissive of the acoustic emission. The cumulative hits behaved differently in 1000 mg/L and 1200 mg/L of NaCl. In the 1000 mg/L NaCl solution, the cumulative hits increased quickly with time and suddenly vised at about 20,000 s, indicating a breakdown of the passive film in a localized area. Then, the cumulative hits increased slowly with time. However, in 1200 mg/L NaCl, the cumulative hits increased rapidly at the beginning Appl. Sci. 2019, 9, 706 6 of 19 then gradually and slowly with time, indicating a relatively stable development of the corrosion attack on the surface after 5000 s. Before that time, the pitting corrosion started from the first second and propagated very fast. On the other hand, the OCP stabilized (even a slightly increasing trend) at a certain time in 1000 mg/L (65,000 s) and 1200 mg/L (30,000 s), which indicated the stage of the stable propagation. The high emissive period of AE was before this stage in these two solutions. The results of the AE monitoring and OCP monitoring were in agreement. It was noticed that the significant increase in the AE hits was earlier than the sharp drop of the OCP in these two experiments. It revealed that the AE monitoring is more sensitive to the corrosion evolution compared with the OCP monitoring. It is interesting that the cumulative hits kept increasing after the stabilization of the OCP curve. The nature of the pitting corrosion is randomly sporadic and stochastic [34]. Therefore, the initiation, accelerated the propagation, and the stable growth/ re-passivation (the corrosion in the pit stopped) of the pitting corrosion is hard to predict. In most circumstances, pitting corrosion happens and propagates at some localized sites independently. The OCP behavior represents the general thermodynamic property of the whole interface, which depends on whether the pitting is meatastable or stable, in which stage—initiation, accelerated propagation, stable growth, or re-passivation—and even the number of active pits. While the increase of cumulative hits stands for the new corrosion events at any local area. These corrosion events refer to any event during corrosion initiation, propagation, and stable growth. While for the whole interface, such corrosion events may have no apparent influence on the OCP in the stable growth stage. However, only the AE signal amplitude of these events exceeds the threshold of the AE acquisition setup, and the recording of AE data can be triggered. That is, the cumulative AE hits in Figure2 only increased for such corrosion events that can generate an AE signal with an amplitude equal or higher than 27 db. Figure2b shows the cumulative absolute energy changes over the time of the AE monitoring. Basically, the higher the AE hits’ activity, the faster the increase of the absolute energy than the other stages in the experiments, as shown in Figure2b, except in the 1200 mg/L NaCl solution. Not only did the acoustic emission of the corrosion event depend on the corrosion stages, and also on the corrosion morphology and other factors, such as the propagation route of pits (i.e., go through the crystalline grain or boundary), but so did the energy of the AE. Although energetic AE signals in pitting normally correspond to the propagation [23,24] and the bursting of the hydrogen bubble, the formation and peeling of thick corrosion products may also generate energetic AE signals if they exist in the 1200 mg/L NaCl solution. As indicated from the above discussion, the AE events are directly associated to the corrosion phenomena, such as passive film breakdown, hydrogen evolution, pit propagation, etc., compared with OCP monitoring, which only reflects the general electrochemical thermodynamic information. Thus, all the stages of corrosion can be detected by AE monitoring. However, the OCP is only sensitive to the corrosion initiation stage because only the corrosion initiation experiences a significant change in the corrosion potential.

3.2. Surface Morphology Monitoring of Pitting Corrosion Figure3 shows the surface morphologies from in-situ monitoring of the propagation of pitting corrosion in different Cl− concentrations at different time intervals of 0 s, 40,000 s, 60,000 s, and 80,000 s, respectively. The surface morphology monitoring experiments were conducted simultaneously with the OCP and AE measurements. After the experiments, the surface morphologies of pits after the removal of loosened corrosion products were observed by SEM, as shown in Figure4. In the presence of 500 mg/L NaCl, several pits can be observed on the surface after 40,000 s of immersion. It can be seen from the images that the pits did not grow as the immersion time increased, indicating the pits re-passivated and would not propagate any more. SEM images also proved, as shown in Figure4a,b, that the pits were open and shallow, without the cover of corrosion products. This type of pitting emitted weak AE signals because it was caused by localized anodic Appl. Sci. 2019, 9, 706 7 of 19 dissolution, which is not an effective generation source of AE. Additionally, some of them may have Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 20 been too weak to pass the threshold of 27 dB in the acquisition setup.

Figure 3. Surface morphologies from in-situ monitoring of the propagation of pitting corrosion of Figure 3. Surface morphologies from in-situ monitoring of the propagation of pitting corrosion of carbon steel with an optical microscope in the solutions with different concentrations of chloride ions: carbon steel with an optical microscope in the solutions with different concentrations of chloride ions: (a1), (a2), (a3), (a4) 500 mg/L NaCl, (b1), (b2), (b3), (b4) 800 mg/L NaCl, (c1), (c2), (c3), (c4) 1000 mg/L (a1), (a2), (a3), (a4) 500 mg/L NaCl, (b1), (b2), (b3), (b4) 800 mg/L NaCl, (c1), (c2), (c3), (c4) 1000 mg/L NaCl, (d1), (d2), (d3), (d4) 1200 mg/L NaCl. NaCl, (d1), (d2), (d3), (d4) 1200 mg/L NaCl. No apparent pits can be observed on the carbon steel surface by an optical microscope before 40,000In sthe in thepresence presence of 500 of 800 mg/L mg/L NaCl, NaCl. several However, pits can the be occluded observed cell on was the formedsurface onafter the 40,000 specimen s of immersion.surface. This It wascan be the seen reason from for the the images increase that of the cumulative pits did not hits grow at theas the beginning immersion of thetime experiment. increased, Afterindicating 40,000 the s, thepits twore-passivated pits gradually and would initiated not and propagate propagated any onmore. the SEM surface. images The also pits proved, continued as shownto grow in as Figure the immersion 4a and 4b, time that increasedthe pits were and op theen corrosion and shallow, damage without area the at 80,000cover of s increased.corrosion Thisproducts. behavior This was type in goodof pitting accordance emitted with weak the AE variation signals of because cumulative it was hits. caused In addition, by localized many corrosion anodic productsdissolution, can which be observed is not an on effective the damage generation area. source The occluded of AE. Additionally, environment some beneath of them the may corrosion have productsbeen too weak formed, to pass which the accelerated threshold of the 27 propagation dB in the acquisition of the pitting setup. corrosion. SEM images showed someNo corrosion apparent products pits can thatbe observed were covered on the on carbon the pit, steel as shownsurface inby Figure an optical4c,d. microscope The carbon before steel 40,000beneath s in the the cover presence continued of 800 to mg/L dissolve NaCl. in theHoweve occludedr, the area, occluded which cell corresponded was formed to on the the continuous specimen surface.increase This of the was cumulative the reason hits for in the Figure increase2. of cumulative hits at the beginning of the experiment. AfterThe 40,000 corrosion s, the two morphology pits gradually evolution initiated and and SEM propagated images of the on specimenthe surface. in 1000The pits mg/L continued of neutral to growNaCl solutionas the immersion are shown time in Figure increased3c1–c4 and and the Figure corrosion4e–f. damage Two apparent area at pits 80,000 can s be increased. observed This on behaviorthe specimen was in surface good ataccordance 40,000 s, whichwith the continued variation toof growcumulative with immersion hits. In addition, time. Manymany corrosion products cancan be be observed observed on on the the damaged damage area area. at 80,000The occluded s. The SEM environment images show beneath that there the werecorrosion some products formed, which accelerated the propagation of the pitting corrosion. SEM images showed some corrosion products that were covered on the pit, as shown in Figure 4c and 4d. The carbon steel beneath the cover continued to dissolve in the occluded area, which corresponded to the continuous increase of the cumulative hits in Figure 2.

Appl. Sci. 2019, 9, 706 8 of 19 shallow and small pits on the sample surface. However, because the solution became more corrosive, the corrosion started earlier and its rate increased. As a result, higher AE activity was recorded at the beginning of the monitoring and the AE hits over a short duration near 20,000 s showed a high absoluteAppl. Sci. energy. 2018, 8, x FOR PEER REVIEW 8 of 20

FigureFigure 4. 4.SEM SEM morphologiesmorphologies of of Q235 Q235 carbon carbon steel steelafter immersion after immersion for 86,400 for s in 86,400 2000 mg/L s in NaHCO 2000 mg/L3 NaHCOand NaCl3 and concentration NaCl concentration of: (a) and of: ( (ba)) 500 and mg/L (b) 500 NaCl, mg/L (c) and NaCl, (d) ( c800) and mg/L (d) NaCl, 800 mg/L (e) and NaCl, (f) 1000 (e) and (f) 1000mg/L mg/L NaCl, NaCl, (g) and (g ()h and) 1200 (h mg/L) 1200 NaCl, mg/L loosened NaCl, loosenedcorrosion corrosionproducts were products removed. were removed.

As impliedThe corrosion by the morphology OCP, the sample evolution of Q235 and SEM carbon images steel of was thecorroded specimen asin soon1000 mg/L as it was of neutral put in the 1200NaCl mg/L solution NaCl are solution, shown asin Figure shown 3c1–c4 in Figure and 3Figured1–d4. 4e–f. Combined Two apparent with pits the cumulativecan be observed hits on curve the in Figurespecimen2, the corrosion surface at initiation 40,000 s, and which propagation continued were to verygrow fast with from immersion the ﬁrst second.time. Many Correspondingly, corrosion the highestproducts AE can activities be observed were on in the the damaged beginning area period, at 80,000 but s. these The SEM did not images have show a high that energy. there were This can be attributedsome shallow to theand shallow small pits and on open the sample shapes surface. of the However, pits, as shown because in the Figure solution4g–h. became The corrosion more mechanismcorrosive, of the the corrosion carbon steel started is due earlier to the and general its rate corrosion increased. at As high a result, Cl− concentrations higher AE activity while was pitting recorded at the beginning− of the monitoring and the AE hits over a short duration near 20,000 s corrosion occurs at low Cl concentrations. Thus, in the NaHCO3 solution with 1200 mg/L NaCl, showed a high absolute energy. As implied by the OCP, the sample of Q235 carbon steel was corroded as soon as it was put in the 1200 mg/L NaCl solution, as shown in Figure 3d1–d4. Combined with the cumulative hits curve

Appl. Sci. 2018, 8, x FOR PEER REVIEW 9 of 20

Appl.in Sci. Figure2019, 9 ,2, 706 the corrosion initiation and propagation were very fast from the first second.9 of 19 Correspondingly, the highest AE activities were in the beginning period, but these did not have a high energy. This can be attributed to the shallow and open shapes of the pits, as shown in Figure the4g–h. localized The corrosioncorrosion tendencymechanism was of the weakened. carbon steel Finally, is due most to ofthe the general surface corrosion of the test at high sample Cl- was coveredconcentrations by corrosion while products, pitting corrosion as shown occurs in Figure at low3d4. Cl− Nevertheless,concentrations. the Thus, non-uniform in the NaHCO coverage3 solution of the corrosionwith 1200 products mg/L NaCl, on the the carbon localized steel corrosion surface tend enhancedency was the weakened. occluded Finally, effect, most which of couldthe surface promote localizedof the test corrosion sample andwas AEcovered generation. by corrosion products, as shown in Figure 3d4. Nevertheless, the non- uniform coverage of the corrosion products on the carbon steel surface enhanced the occluded effect, 3.3.which Waveform could Processing promote localized corrosion and AE generation.

3.3.The Waveform acoustic Processing signals from experiments were recorded by the AE system. Figure5a shows a typical waveform of the acoustic signal of carbon steel in NaHCO + NaCl solution. It shows that some The acoustic signals from experiments were recorded by3 the AE system. Figure 5a shows a information of the waveform, the beginning and end, was not useful. So, additional processing of the typical waveform of the acoustic signal of carbon steel in NaHCO3 + NaCl solution. It shows that signalssome was information necessary of for the the waveform, preparation the beginning of further an analysis.d end, was Herein, not useful. three So, steps, additional including processing pre-trigger removal,of the tail-cutting,signals was andnecessary shape for preserving the preparation interpolation of further (SPI), analysis. were applied Herein, to three process steps, the including waveform in Matlab.pre-trigger In pre-trigger removal, removal, tail-cutting, a value and tellsshape the preservi softwareng interpolation how long to record(SPI), were (in µ appliedsec) before to process the trigger pointthe (the waveform point at in which Matlab. the In threshold pre-trigger is removal, exceeded), a value and istells aimed the software at removing how digitallong to noiserecord from (in the acquisition.µsec) before Tail-cutting the trigger was point used (the to point remove at which the “zero-padding”,the threshold is exceeded), which may and haveis aimed been at appliedremoving at the enddigital of some noise waveforms from the duringacquisition. the acquisitionTail-cutting processwas used (Figure to remove5b). SPIthe “zero-padding”, was an effective which tool to may ensure thathave each been waveform applied had at the the end same of some number waveforms of points during to be stored,the acquisition which isprocess based (Figure on the Piecewise5b). SPI was Cubic Hermitean effective Interpolating tool to ensure Polynomial that each (PCHIP) waveform technique had the same and number the Weierstrass of points approximationto be stored, which theorem. is based on the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) technique and the The waveform after processing is shown in Figure6. In this study, wavelet denoising was performed Weierstrass approximation theorem. The waveform after processing is shown in Figure 6. In this by the Matlab wavelet tool box (Figure7). The treated waveform by denoising was stored for the study, wavelet denoising was performed by the Matlab wavelet tool box (Figure 7). The treated processingwaveform and by extraction denoising ofwas the stored parameters. for the processing and extraction of the parameters.

0.04 (a)

0.02

Amplitude/ V -0.02

-0.04 Appl. Sci. 2018, 8, x FOR0 PEER REVIEW500 1000 1500 2000 2500 10 of 20 Time/ μs 0.04 (b)

0.02

Amplitude/ V -0.02

-0.04 0 500 1000 1500 2000 2500 Time/ μs

FigureFigure 5. The5. The waveform waveform processing processing ofof aa typicaltypical acoustic acoustic signal signal obtained obtained from from Q235 Q235 carbon carbon steel steel in in

NaHCONaHCO3 +3NaCl + NaCl solution: solution: ( a(a))original, original, (b)) pre-trigger pre-trigger removal removal and and tail tail cutting. cutting.

0.04

0.02

Amplitude/ V -0.02

-0.04 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs Figure 6. The processed waveform of a typical acoustic signal obtained from Q235 carbon steel in

NaHCO3 + NaCl solution (after SPI and wavelet denoising).

Appl. Sci. 2018, 8, x FOR PEER REVIEW 10 of 20

0.04 (b)

0.02

Amplitude/ V -0.02

-0.04 0 500 1000 1500 2000 2500 Time/ μs

Figure 5. The waveform processing of a typical acoustic signal obtained from Q235 carbon steel in

Appl. Sci.NaHCO2019, 9,3 706 + NaCl solution: (a) original, (b) pre-trigger removal and tail cutting. 10 of 19

0.04

0.02

Amplitude/ V -0.02

-0.04 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs FigureFigure 6. 6.The The processed processed waveform waveform of of a a typical typical acoustic acoustic signal signal obtained obtained from from Q235 Q235 carbon carbon steel steel in in Appl.NaHCONaHCO Sci. 20183 +3 +, NaCl8 NaCl, x FOR solution solution PEER REVIEW (after (after SPI SPI and and wavelet wavelet denoising). denoising). 11 of 20

FigureFigure 7. Denoising 7. Denoising method method for for a a typical typical signalsignal of the acoustic acoustic emission emission of carbon of carbon steel steel in NaHCO in NaHCO3 + 3 + NaClNaCl solution. solution. (a) ( Signala) Signal before before denoising, denoising, ((bb)) signalsignal after after denoising. denoising.

3.4. Clustering Analysis A function was established to input waveform information, time and system information including the maximum duration, hit definition time (HDT), hit lockout time (HLT), peek definition time (PDT), sampling frequency, and the threshold, to Matlab. Some AE parameters were used for the clustering analysis, for instance, the “count”, the “duration”, and the “average frequency”. The count of the AE event is the number of times the signal rises and crosses the threshold of the AE setup. The duration of the AE event is the time between the rising edge of the first count and the falling edge of the last count. The average frequency (Hz) was calculated from NAE/tAE to estimate the characteristic frequency of an AE event. These parameters were recorded by the AE acquisition system in-situ and extracted in Matlab after waveform processing, respectively. After the extracting feature, these new parameters were plotted in additional correlation diagrams. The comparison between the original parameters and the new parameters under different corrosion conditions are shown in Figure 8 and Figure 9. These results showed that the performing of the developed signal processing can effectively classify AE signals in the correlation diagram.

Appl. Sci. 2019, 9, 706 11 of 19

3.4. Clustering Analysis A function was established to input waveform information, time and system information including the maximum duration, hit definition time (HDT), hit lockout time (HLT), peek definition time (PDT), sampling frequency, and the threshold, to Matlab. Some AE parameters were used for the clustering analysis, for instance, the “count”, the “duration”, and the “average frequency”. The count of the AE event is the number of times the signal rises and crosses the threshold of the AE setup. The duration of the AE event is the time between the rising edge of the first count and the falling edge of the last count. The average frequency (Hz) was calculated from NAE/tAE to estimate the characteristic frequency of an AE event. These parameters were recorded by the AE acquisition system in-situ and extracted in Matlab after waveform processing, respectively. After the extracting feature, these new parameters were plotted in additional correlation diagrams. The comparison between the original parameters and the new parameters under different corrosion conditionsAppl. Sci. 2018 are, shown 8, x FOR in PEER Figures REVIEW8 and 9. These results showed that the performing of the developed12 of 20 signal processing can effectively classify AE signals in the correlation diagram.

s μ Duration/

Figure 8. Analysis of the parameters of the signals of Q235 carbon steel in 2000 mg/L NaHCO3 Figure 8. Analysis of the parameters of the signals of Q235 carbon steel in 2000 mg/L NaHCO3 + + 800 mg/L NaCl: (a) and (c) original relationship of the duration-average frequency and 800mg/L NaCl: (a) and (c) original relationship of the duration-average frequency and durationduration-counts, (b) and (d) relationship of the duration-average frequency and duration-counts counts, (b) and (d) relationship of the duration-average frequency and duration-counts after the after the extracting feature. extracting feature. Based on the analysis of the relationship between the duration and average frequency of the AE waveforms, the AE signals of Q235 carbon steel in different corrosive media (2000 mg/L NaHCO3 + 800 mg/L NaCl and 2000 mg/L NaHCO3 + 1000 mg/L NaCl solution) during the pitting process were classiﬁed into three types: High duration with high frequency, high duration with low frequency, and low duration with low frequency. In addition, the three groups of signals were identiﬁed according to the duration versus counts correlation diagram: Low duration with low counts, high duration with low counts, and high duration with high counts. The representative waveforms of the three types of signal are shown in Figure 10. The low duration (<2000 µs) with low counts (<50) signals and high duration (>2000 µs) with low counts (<50) signals appeared most frequently at the beginning of the experiment, indicating that they belong

Figure 9. Analysis of the parameters of the signals of Q235 carbon steel in 2000 mg/L NaHCO3 + 1000 mg/L NaCl: (a) and (c) original relationship of the duration-average frequency and duration-counts,

Appl. Sci. 2018, 8, x FOR PEER REVIEW 12 of 20

s μ Duration/

Appl. Sci. 2019, 9, 706 12 of 19 to the corrosion event of the surface breakdown. They were the burst signal (Figure 10a, duration 492 µs, counts 23) and asymmetric signal (Figure 10b, duration 2732 µs, counts 44). Most of the high duration (>2000 µs) and high counts (50–100) signals occurred during the accelerated propagation of pitting corrosion. They were resonant signals (Figure 10c, duration 2336 µs, counts 98). In the stable growth processFigure 8. of Analysis the pitting of the corrosion parameters after of the signal accelerateds of Q235 propagation, carbon steel thein 2000 AE waveformsmg/L NaHCO were3 + Appl. Sci. 2018, 8, x FOR PEER REVIEW 13 of 20 also resonant800mg/L signals. NaCl: However,(a) and (c) theoriginal signal relationship feature was of the more dura complextion-average than frequency that in the and accelerated duration- counts, (b) and (d) relationship of the duration-average frequency and duration-counts after the propagation(b) and process (d) the relationship (Figure 10 d,of durationduration-average 1878 µ s,freque countsncy 77),and duration-counts which may be after due the to theextracting combination of the breakdownfeature.extracting offeature. corrosion products and the growth of the pits at the same time. The above result is consistent with other studies on the acoustic emissions of pitting corrosion [23,35,36]. Based on the analysis of the relationship between the duration and average frequency of the AE waveforms, the AE signals of Q235 carbon steel in different corrosive media (2000 mg/L NaHCO3 + 800 mg/L NaCl and 2000 mg/L NaHCO3 + 1000 mg/L NaCl solution) during the pitting process were classified into three types: High duration with high frequency, high duration with low frequency, and low duration with low frequency. In addition, the three groups of signals were identified according to the duration versus counts correlation diagram: Low duration with low counts, high duration with low counts, and high duration with high counts. The representative waveforms of the three types of signal are shown in Figure 10. The low duration (<2000 µs) with low counts (<50) signals and high duration (>2000 µs) with low counts (<50) signals appeared most frequently at the beginning of the experiment, indicating that they belong to the corrosion event of the surface breakdown. They were the burst signal (Figure 10a, duration 492 µs, counts 23) and asymmetric signal (Figure 10b, duration 2732 µs, counts 44). Most of the high duration (>2000 µs) and high counts (50–100) signals occurred during the accelerated propagation of pitting corrosion. They were resonant signals (Figure 10c, duration 2336 µs, counts 98). In the stable growth process of the pitting corrosion after the accelerated propagation, the AE waveforms were also resonant signals. However, the signal feature was more complex than that in the accelerated propagation process (Figure 10d, duration 1878 µs, counts 77), which may be due to the combination of the breakdown of corrosion products and the growth of the pits at the same time. The above result is consistent with other studies on the acoustic emissions of pitting corrosion [23,35,36]. Many researchers have tried to quantitatively correlate AE parameters to corrosion based on parameter recognition or clusters. A good correlation between the cumulative hits of surficial oxide film rupture and the corrosion current density in carbon corrosion was reported [35]. However, to the emission of AE is not only affected by corrosion phenomena, but also by the mechanical condition Figure 9. Analysis of the parameters of the signals of Q235 carbon steel in 2000 mg/L NaHCO3 of +steel 1000Figure structure. mg/L 9. Analysis NaCl: It is very (ofa) the anddifficult parameters (c) originalto determin of the relationship signale thes ofcorrosion Q235 of the carbon rate duration-average steelby AE in 2000in practical mg/L frequency NaHCO settings. and3 + 1000In addition,duration-counts,mg/L though NaCl: the(a ()b and )recognition and (c) ( doriginal) the and relationship relationship identification of of duration-average the of duration AE signals-average frequency could frequency offer and useful and duration-counts duration-counts, information regardingafter the extractingthe corrosion feature. mechanism, identification of the group of signals that represents the corrosion rate requires further investigation.

0.015 (a) 0.01

0.005

-0.005 Amplitude/V -0.01

-0.015 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs Figure 10. Cont.

Appl. Sci. 2019, 9, 706 13 of 19 Appl. Sci. 2018, 8, x FOR PEER REVIEW 14 of 20

0.003 (b) 0.002

0.001

-0.001 Amplitude/ V Amplitude/ -0.002

-0.003 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs

0.015 (c) 0.01

0.005

-0.005 Amplitude/ V Amplitude/ -0.01

-0.015 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs

0.004 (d)

0.002

Amplitude/ V -0.002

-0.004 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time/ μs Figure 10. Waveforms from top to bottom: (a) and (b) low duration with low frequency and low Figure 10. Waveforms from top to bottom: (a) and (b) low duration with low frequency and low counts, (c) high duration with low frequency and low counts, (d) high duration with high frequency counts, (c) high duration with low frequency and low counts, (d) high duration with high frequency and high counts. and high counts. Many researchers have tried to quantitatively correlate AE parameters to corrosion based on 3.5. Corrosion Type Identification parameter recognition or clusters. A good correlation between the cumulative hits of surficial oxide film ruptureThe typical and the methods corrosion for currentdistinguishing density corrosion in carbon types corrosion includes was AE reported parameters [35]. cluster However, (i.e., to k- the emissionmeans) ofand AE the is notensemble only affected learning by method corrosion (i.e., phenomena, random forest). but alsoThese by methods the mechanical only utilize condition some of steelparameters structure. of It the is very AE difficultwaveform to determine(raw or pre-treated) the corrosion or rely rate on by big AE data. in practical To identify settings. the Indifferent addition, though the recognition and identification of AE signals could offer useful information regarding the Appl. Sci. 2019, 9, 706 14 of 19 corrosion mechanism, identification of the group of signals that represents the corrosion rate requires further investigation.

3.5. Corrosion Type Identification The typical methods for distinguishing corrosion types includes AE parameters cluster (i.e., k-means) and the ensemble learning method (i.e., random forest). These methods only utilize some parameters of the AE waveform (raw or pre-treated) or rely on big data. To identify the different corrosion types in the corrosion process fast and effectively, a 2D pattern recognition algorithm was established by Matlab. This processing algorithm provides a new feasible method for the identification of different acoustic emission data directly from the waveform of the AE signal without the extraction of AE parameters. In 2D pattern recognition analysis, the luminance information of the object surface is related to the illumination and reflection coefficient. Otherwise, the structure and illumination of the objects in the scene are independent. The reflection coefficient and the related objects are the same. We can explore the structure information in an image by separating the illumination effects of objects. Here, the object and structure relate to the brightness and contrast as the structure information of the image definition. Because the brightness and contrast of a scene are always changing, we can perform local processing to obtain more accurate results. The measurement of similarity is composed of three modules: Luminance, contrast ratio, and structure. Their functions are defined as follows. First, for the discrete signal, we used the average gray level as a measure of the intensity of the estimation, as function (1) shows, and the brightness contrast function, L(x, y), is a function about µx and µy: N µx = (1/N) ∑ xi (1) 1 Then, the average gray value should be removed from the signal based on the measurement system. For the discrete signal, x − µx, the standard deviation can be used to do the contrast measure, such as function (2) shows, and the contrast function, c(x, y), of the contrast is a function of σx and σy:

N ! 1/2 2 = 1/N − 1 ∑(xi − µx) (2) i=1

Next, the signal is divided by its standard deviation, and the contrast function structure is deﬁned ( − ) (y−µy) as a function of x µx and . σx σy Finally, the three contrast modules are combined into a complete similarity measure function:

S(x, y) = (l(x, y), c(x, y), s(x, y)) (3)

S (x, y) should meet the following three conditions: Symmetry: S(x, y) = S(y, x); bounded property: S(x, y) ≤ 1; and maximum uniqueness: S(x, y) = 1, when and only when x = y. Then, the three contrast functions are deﬁned, and ﬁnally they are combined to obtain the following similarity function:

SSIM(x, y) = [l(x, y)]α[c(x, y)]β[s(x, y)]γ (4)

When α = β = γ = 1, a function would be obtained as follows:

2 2 2 2 SSIM(x, y) = 2µxµy + C1 2σxσy + C2 / µx + µy + C1 σx + σy + C2 (5) Appl. Sci. 2019, 9, 706 15 of 19

Other corrosion types were monitored by AE, aiming to calculate the similarities among them. The pitting corrosion of stainless steel in the presence of different concentrations of chloride ions and the uniform corrosion of carbon steel in sulphuric acid solution was monitored by the same device as Figure1. The specimens of the crevice corrosion of stainless steel were pretreated based on ASTM G39-1999(2011) and monitored by the method of Kim [18], and the AE waveforms of different types of corrosion were arranged in a time sequence. Figure 11 shows the continuous AE waveform graphics of the different corrosion experiments. These continuous AE waveforms were obtained by arranging the waveformAppl. Sci. 2018 of, 8, eachx FORAE PEER event REVIEW one by one in the time sequence. These waveform graphics were16 importedof 20 into Matlab at ﬁrst, then the 2D pattern recognition algorithm was performed as described by formula (1)described to (5) toby obtain formula the (1) similarities to (5) to obtain of the the different similarities graphics. of the The different calculation graphics. results The arecalculation exhibited in Tableresults3. are exhibited in Table 3.

0.2 (a)

0.1

Amplitude/ V Amplitude/ -0.1

-0.2 0 1 2 3 4 5 6 7 8 Time/ s x 105 0.2 (b)

0.1

Amplitude/ V -0.1

-0.2 0 1 2 3 4 5 6 7 8 Time/ s x 105 0.8 (c) 0.6 0.4 0.2 0 -0.2

Amplitude/ V -0.4 -0.6 -0.8 0 1 2 3 4 5 6 7 8 Time/ s x 105 Figure 11. Cont.

Appl. Sci. 2019, 9, 706 16 of 19 Appl.Appl. Sci.Sci. 20182018,, 88,, xx FORFOR PEERPEER REVIEWREVIEW 1717 ofof 2020 Appl.Appl.Appl.Appl.Appl. Sci.Sci. Sci.Sci. Sci. 20182018 20182018 2018,,, 88,, , ,8, 8 x8x,x, , x xFOR FORFORx FOR FORFOR PEERPEERPEER PEERPEERPEER REVIEWREVIEWREVIEW REVIEWREVIEWREVIEW 17 1717of1717 of20of ofof 2020 2020

0.10.1 0.10.10.10.1 0.10.1 (d) (d)(d)(d)(d)

0.050.050.05 0.050.050.050.05

00 0 0000 Amplitude/ V Amplitude/ V Amplitude/ V -0.05 Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V -0.05 -0.05Amplitude/ V Amplitude/ V -0.05-0.05-0.05-0.05-0.05

-0.1 -0.1 -0.1-0.1-0.1 0 1 2 3 4 5 6 7 8 -0.1-0.1-0.1-0.10 1 2 3 4 5 6 7 8 -0.1000 111 222 333 444 555 666 777 5 888 00 11 22 33 Time/44 s 55 66 77 x 10 5588 Time/ s x 10 55 55 Time/Time/Time/Time/ ss ss xx x x1010 10105 Time/ s x 10 0.1 0.10.10.1 (e) 0.10.1 (e)(e) (e)(e)(e)(e) 0.05 0.050.05 0.050.050.050.05

0 00 0000

Amplitude/ V -0.05 Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V Amplitude/ V

Amplitude/ V -0.05

-0.05Amplitude/ V Amplitude/ V -0.05-0.05-0.05-0.05-0.05 -0.1 0 1 2 3 4 5 6 7 8 -0.1 5 -0.1-0.1-0.1-0.1 Time/ s x 10 -0.1000 111 222 333 444 555 666 777 888 00 11 22 33 44 55 66 77 585 8 x 1055 55 Time/Time/Time/ ss s xx x1010 105 FigureFigure 11. Different continuous continuous AE AE graphics: graphics: (a) ( apitting) pittingTime/Time/ corrosion corrosion s s of carbon of carbon steel, steel, (b) pitting (b) pitting corrosionxx 10 corrosion10 FigureFigureofof stainlessstainless 11.11. DifferentDifferent steel, ( ( cc continuouscontinuous)) crevice crevice corrosion corrosion AEAE graphics:graphics: of of stainless stainless ((aa)) pittingpitting steel, steel, ( d corrosioncorrosion) ( duniform) uniform of ofcorrosion carboncarbon corrosion steel,ofsteel, carbon of ( carbon(bb)) pittingpittingsteel, steel, (e corrosion)corrosion noise. (e) noise. FigureFigureFigureFigure 11.11. 11.11. DifferentDifferent DifferentDifferent continuouscontinuous continuouscontinuous AEAE AEAE graphics:graphics: graphics:graphics: ((a a (()a))a pitting)pitting)pitting pittingpitting corrosioncorrosioncorrosion corrosioncorrosion ofofof ofof carboncarboncarbon carboncarbon steel,steel,steel, steel,steel, (((b b ((b))b) pitting)pitting)pitting pittingpitting corrosioncorrosioncorrosion corrosioncorrosion ofof stainlessstainless steel,steel, ((cc)) crevicecrevice corrosioncorrosion ofof stainlessstainless steel,steel, ((dd)) uniformuniform corrosioncorrosion ofof carboncarbon steel,steel, ((ee)) noise.noise. ofofofof stainlessstainless stainlessstainless steel,steel, steel,steel, ((c c (())c)c crevice)crevice)crevice crevicecrevice corrosioncorrosioncorrosion corrosioncorrosion ofofof ofof stainlessstainlessstainless stainlessstainless steel,steel,steel, steel,steel, (((d d ((d)d)) uniform)uniform)uniform uniformuniform corrosioncorrosioncorrosion corrosioncorrosion ofofof ofof carboncarboncarbon carboncarbon steel,steel,steel, steel,steel, (((e e (()e))e noise.)noise.)noise. noise.noise. TableTable 3. 3. TheThe similarity similarity of ofthe the different different types types of corrosion. of corrosion. TablePittingTable 3. 3.Corrosion TheThe similaritysimilarity Crevice ofof thethe Corrosion differentdifferent typestypesUniform ofof corrosion.corrosion. Corrosion TableTableTableTable 3.3. 3.3. TheThe TheThe similaritysimilarity similaritysimilarity ofof ofof thethe thethe differentdifferent differentdifferent typestypes typestypes ofof ofof corrosion.corrosion. Uniformcorrosion.corrosion. Noise PittingCarbon Corrosion Steel CreviceStainless Corrosion Steel Carbon Steel PittingPittingPitting CorrosionCorrosion Corrosion CreviceCreviceCrevice CorrosionCorrosion Corrosion UniformUniformCorrosionUniform CorrosionCorrosion Corrosion Carbon Noise PittingPittingCarbon Corrosion Corrosion Steel CreviceCreviceStainless Corrosion Corrosion Steel UniformUniform Corrosion Corrosion NoiseNoise CarbonCarbon SteelSteel StainlessStainless SteelSteel CarbonCarbonSteel SteelSteel NoiseNoiseNoiseNoise Pitting corrosion CarbonCarbonCarbonCarbon SteelSteel SteelSteel StainlessStainlessStainlessStainless SteelSteel SteelSteel CarbonCarbonCarbonCarbon SteelSteel SteelSteel Stainless steel 83.23% 78.03% 50.12% 27.53% PittingPittingPitting corrosioncorrosion corrosion PittingPittingPitting corrosioncorrosion corrosion 27.53% StainlessStainless steelsteel 83.23%83.23% 78.03%78.03% 50.12%50.12% 27.53%27.53%27.53%27.53% StainlessStainlessStainlessStainless steelsteel steel steel 83.23%83.23%83.23%83.23% 78.03%78.03%78.03%78.03% 50.12%50.12%50.12%50.12% 27.53%

Pitting corrosion

Carbon steel 85.51% 52.16% 48.74% PittingPitting corrosioncorrosion PittingPittingPittingPitting corrosioncorrosion corrosion corrosion CarbonCarbon steelsteel 85.51%85.51%85.51%85.51% 52.16%52.16% 48.74% 48.74% CarbonCarbonCarbonCarbon steelsteel steel steel 85.51% 52.16%52.16%52.16%52.16% 48.74% 48.74% 48.74% 48.74% Crevice corrosion 35.41% 45.13% Stainless steel

CreviceCreviceCrevice corrosioncorrosion corrosion CreviceCrevice corrosion corrosion 35.41%35.41%35.41% 45.13%45.13% StainlessUniformStainless corrosion steelsteel 35.41%35.41%35.41% 45.13%45.13%45.13%45.13% StainlessStainlessStainlessStainless steelsteel steel steel 45.13% 42.13% Carbon steel

UniformUniform corrosioncorrosion UniformUniformUniformUniform corrosioncorrosion corrosioncorrosion 42.13% UniformCarbonAccording corrosion steel to the calculation results, there was a high similarity between the pitting corrosion42.13%42.13%42.13%42.13% of CarbonCarbonCarbonCarbon steelsteel steelsteel 42.13% carbonCarbonCarbon steel steel steel and that of stainless steel. The occluded cell of the pitting corrosion on carbon steel was

notAccordingAccording stable in the toto thetheperformed calculationcalculation experiments. results,results, therethere The wasrepewas ating aa highhigh process similaritysimilarity of the betweenbetween occluded thethe cell pittingpitting formation corrosioncorrosion and ofof AccordingAccordingAccordingAccording toto toto thethe thethe calculationcalculation calculationcalculation results,results, results,results, therethere therethere waswas waswas aa a ahighhigh highhigh similaritysimilarity similaritysimilarity betweenbetween betweenbetween thethe thethe pittingpitting pittingpitting corrosioncorrosion corrosioncorrosion ofof ofof carboncarbonrupture steelsteel caused andand that thatthe ofopenof stainlestainle pits sssson steel.steel. the surface TheThe occludedoccluded of the ca cellcellrbon ofof steel. thethe pitti pittiIn contrast,ngng corrosioncorrosion the pitting onon carboncarbon corrosion steelsteel of waswas carboncarboncarboncarbonAccording steelsteel steelsteel andand andand thatthat to thatthat the ofof ofof calculationstainlestainle stainlestainlessssssss steel.steel. steel.steel. results, TheThe TheThe thereoccludedoccluded occludedoccluded was cellcell a cellcell high ofof ofof thethe similarity thethe pittipitti pittipittingngngng betweencorrosioncorrosion corrosioncorrosion the onon onon pitting carboncarbon carboncarbon corrosion steelsteel steelsteel waswas waswas of notnotstainless stablestable in insteel thethe had performedperformed a relatively experiments.experiments. strong occlusion TheThe repe repeeffectatingating, such processprocess that the ofof corrosion thethe occludedoccluded pit of thecellcell stainless formationformation steel andand notnotnotnotcarbon stablestable stablestable steel inin inin thethe andthethe performedperformed performedperformed that of stainless experiments.experiments. experiments.experiments. steel. The TheThe TheThe occluded reperepe reperepeatingatingatingating cell processprocess processprocess of the pittingofof ofof thethe thethe occludedoccluded corrosion occludedoccluded cellcell oncellcell carbonformationformation formationformation steel andand andand was rupturerupture causedcaused thethe openopen pitspits onon thethe surfacesurface ofof thethe cacarbonrbon steel.steel. InIn contrast,contrast, thethe pittingpitting corrosioncorrosion ofof rupturerupturerupturerupturerupture causedcausedcaused causedcaused thethethe thethe openopenopen openopen pitspitspits pitspits ononon onon thethethe thethe surfacesurfacesurface surfacesurface ofofof ofof thethethe thethe cacaca cacarbonrbonrbonrbonrbon steel.steel.steel. steel.steel. InInIn InIn contrast,contrast,contrast, contrast,contrast, thethethe thethe pittingpittingpitting pittingpitting corrosioncorrosioncorrosion corrosioncorrosion ofofof ofof stainlessstainless steelsteel hadhad aa relativelyrelatively strongstrong occlusionocclusion effecteffect,, suchsuch thatthat thethe corrosioncorrosion pitpit ofof thethe stainlessstainless steelsteel stainlessstainlessstainlessstainless steelsteel steelsteel hadhad hadhad aa a arelativelyrelatively relativelyrelatively strongstrong strongstrong occlusionocclusion occlusionocclusion effecteffect effecteffect,,, such,such,such suchsuch thatthatthat thatthat thethethe thethe corrosioncorrosioncorrosion corrosioncorrosion pitpitpit pitpit ofofof ofof thethethe thethe stainlessstainlessstainless stainlessstainless steelsteelsteel steelsteel

Appl. Sci. 2019, 9, 706 17 of 19 not stable in the performed experiments. The repeating process of the occluded cell formation and rupture caused the open pits on the surface of the carbon steel. In contrast, the pitting corrosion of stainless steel had a relatively strong occlusion effect, such that the corrosion pit of the stainless steel often had a small mouth with relatively larger depth. However, these two kinds of pitting corrosion were all caused by the occlusion effect, from which they had a similar acoustic emission process (83.23% similarity) as shown in Table3. In addition, the corrosion process of the 2D images of the pitting corrosion and crevice corrosion also had a high degree of similarity (73.03%), which is related to their corrosion mechanisms being relatively similar [34]. On the other hand, the graphics of the uniform corrosion had a low similarity with the other types of corrosion, indicating that this data processing approach can identify localized corrosion from uniform corrosion. In addition, the AE graphic of the artiﬁcial noise from the stirring of the solution also had low similarities with all the corrosion graphics.

4. Conclusions

The pitting corrosion of Q235 carbon steel in NaHCO3 + NaCl solutions was studied by in-situ monitoring of the AE technique and OCP simultaneously. The concentration of NaCl had a pronounced influence on the OCP evolution. In 500 mg/L NaCl, the OCP varied in a very narrow range, indicating a relatively stable state. However, with the increase of the NaCl concentration, the OCP dropped significantly and stabilized at a rather negative potential. The AE monitoring results were in accordance with the results of the OCP monitoring. However, the OCP only presented thermodynamic information of the corrosion in the interface, while the AE sensor detected the breakdown of the passive film and small damage in the occluded pits under the metallic surface, and the relative events. The AE signals of pitting corrosion on carbon steel were classified into three types by waveform parameters clustering after AE waveform processing, including pre-treatment, shape preserving interpolation, and denoising. The result indicated that the developed signal processing is a highly efficient method for the classification of AE signals and preparation of the waveform for further data analysis. A method based on 2D pattern recognition was established for the identification of different types of corrosion in Matlab. The analysis results showed that the method can be used to distinguish between uniform corrosion and localized corrosion effectively, while it was not very effective for distinguishing different localized corrosion. The AE technique could be applied to in-situ monitoring of the corrosion of carbon steel or stainless steel containers in different industries and for the reinforcement of steel bars for construction. However, the main obstacle to the use of AE monitoring is environmental noise, which make data processing difficult.

Author Contributions: Conceptualization, J.T. and H.W.; methodology, J.T. and H.W.; software, J.T. and H.W.; investigation, J.T., H.W. and G.C.; data curation, J.L.; writing—original draft preparation, J.T. and Y.W.; writing—review and editing, J.T. and Y.W.; visualization, J.T. and J.L.; supervision, H.W. Funding: This research was funded by Applied Basic Research Programs of Science and Technology Department of Sichuan Province, grant number 2017JY0044 and Project Funding to Scientific Research Innovation Team of Universities Affiliated to Sichuan Province, grant number 18TD0012. Conflicts of Interest: The authors declare no conflict of interest.

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