processes Review Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review Qian Lv 1, Xiaoling Yu 1,*, Haihui Ma 1, Junchao Ye 1, Weifeng Wu 1 and Xiaolin Wang 2 1 School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China; [email protected] (Q.L.); [email protected] (H.M.); [email protected] (J.Y.); [email protected] (W.W.) 2 School of Engineering, University of Tasmania, Hobart, TAS 7001, Australia; [email protected] * Correspondence: [email protected] Abstract: Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed. Keywords: reciprocating compressor; condition monitoring; fault diagnosis; machine learning Citation: Lv, Q.; Yu, X.; Ma, H.; Ye, J.; 1. Introduction Wu, W.; Wang, X. Applications of The reciprocating compressor (RC) is a key piece of equipment in petroleum and Machine Learning to Reciprocating chemical industries. If the RC does not operate in the rated efficiency, it will lead to great Compressor Fault Diagnosis: A economic loss to the company. Sometimes RCs are used to compress inflammable and ex- Review. Processes 2021, 9, 909. plosive gases working under high pressures and temperatures, such as hydrogen, ethylene, https://doi.org/10.3390/pr9060909 and natural gas, which would threat human life once the machine malfunctions [1]. Fur- thermore, due to the intricate structure of the compressor, a large amount of wearing parts, Academic Editor: Jie Zhang and the complicated interactional relationship between moving parts of the compressor, it is essential to monitor the compressor operating condition and detect failures of RCs Received: 21 April 2021 accurately and in a timely manner [2]. Accepted: 18 May 2021 Traditionally, the fault diagnosis of RCs was carried out by engineers who had exten- Published: 21 May 2021 sive experience and knowledge. As an example, an experienced engineer is able to detect the faults of RCs according to the abnormal noise occurring in sound signals [3,4]. However, Publisher’s Note: MDPI stays neutral users still tend to predict faults more accurately before the RC faults occur. This will reduce with regard to jurisdictional claims in published maps and institutional affil- the operation and maintenance (O&M) costs by using an automatic condition classification iations. system [5]. Recently, with the rapid development of artificial intelligence (AI), machine learning (ML) has turned out to make a great difference in machine fault diagnosis and prediction [6]. Zhang et al. [6] discussed the computational intelligence techniques utilized in machinery condition monitoring and fault diagnosis. A comprehensive overview of some common natural computing methods and their applications in mechanical systems Copyright: © 2021 by the authors. was made by Worden et al. in [7]. Some other researchers [8] intend to develop a review Licensee MDPI, Basel, Switzerland. of bearing fault diagnosis based on deep learning (DL), whereas very few reviews were This article is an open access article distributed under the terms and conducted for intelligent fault diagnosis of reciprocating compressors. conditions of the Creative Commons This paper reviews the recent research and development of fault diagnosis technolo- Attribution (CC BY) license (https:// gies for reciprocating compressors utilizing machine learning in terms of theoretical studies creativecommons.org/licenses/by/ and applications. To finish this review, search terms such as artificial intelligence, machine 4.0/). learning, reciprocating compressor, fault diagnosis, fault detection, monitoring system, etc., Processes 2021, 9, 909. https://doi.org/10.3390/pr9060909 https://www.mdpi.com/journal/processes Processes 2021, 9, x FOR PEER REVIEW 2 of 15 machine learning, reciprocating compressor, fault diagnosis, fault detection, monitoring system, etc., were used in the process of searching literature. The filtering mechanism is mainly based on multiple combinations of the search terms mentioned above. Most of the literature surveyed is from journals, in which the English literature was searched using Web of Science and the Chinese literature was searched using China National Knowledge Infrastructure (CNKI). The theories of RCs and ML methods were mostly from books. After the searching process, the literature was classified into several categories based on Processes 2021, 9, 909 ML methods applied and the main monitoring parameters. The following reviewing2 sec- of 14 tions are also introduced according to the categories. After the review of literature, the advantages and limitations of the ML technologies, comparison of different monitoring parameters,were used in and the an process overlook of searching of future literature.research are The also filtering discussed. mechanism is mainly based on multiple combinations of the search terms mentioned above. Most of the literature sur- 2.veyed Overview is from of journals, Reciprocating in which Compressors the English literature was searched using Web of Science andFigure the Chinese 1 shows literature a single-stage was searched reciprocating using China compressor, National which Knowledge is mainly Infrastructure made up of two(CNKI). valves, The a theories piston, a of cylinder, RCs and a ML piston methods rod, a were crosshead, mostly froma connecting books. After rod, theand searching a crank- shaft.process, The the crankshaft literature wasis driven classified by intomotor, several then categories the crankshaft based onreciprocates ML methods the appliedpiston throughand the mainthe slide-crank monitoring mechanism, parameters. so The that following the piston reviewing can compress sections gas are in alsothe cylinder introduced to aaccording designated to high the categories. pressure [9]. After RCs the can review be applied of literature, in chemical, the advantages refining, and and petrochem- limitations icalof the plants, ML technologies,and they can compress comparison almost of different any gas monitoring mixture from parameters, vacuum to and over an 3000 overlook atm. of futureThe faults research of arecompressors also discussed. are caused by failures of different components. In [10], Kostyukov performed a survey into the fault causes of reciprocating compressors based on2. Overviewconsumers of and Reciprocating manufacturers Compressors of RCs. The results showed that one of the main reasons for compressorFigure1 shows failure a is single-stage valves, and reciprocating it makes up 36%. compressor, Piston-cylinder which units is mainly also constitute made up overof two 30% valves, of all afaults, piston, where a cylinder, the failures a piston of rings rod, account a crosshead, for 25%. a connecting Failures in rod, the andslide- a crankcrankshaft. mechanism The crankshaft and cranking is driven mechanism by motor, are thenalso significant the crankshaft [10]. reciprocates To monitor thecompres- piston sorthrough conditions, the slide-crank many kinds mechanism, of sensors so were that theused piston in fault can detection compress systems, gas in the such cylinder as vibra- to a tiondesignated sensors, high temperature pressure sensor, [9]. RCs pressure can be applied sensor, indisplacement chemical, refining, sensor, andacoustic petrochemical emission sensor,plants, and so they on. can compress almost any gas mixture from vacuum to over 3000 atm. Figure 1. Principle of a one-stage reciprocating compressor. 1—suction valve; 2—discharge valve; Figure3—piston; 1. Principle 4—cylinder; of a one-stage 5—piston reciprocating rod; 6—crosshead; compressor. 7—connecting 1—suction rod; valve; 8—crankshaft. 2—discharge valve; 3—piston; 4—cylinder; 5—piston rod; 6—crosshead; 7—connecting rod; 8—crankshaft. The faults of compressors are caused by failures of different components. In [10], 3.Kostyukov Overview performed of the Four a surveyMajor intoMachine the fault Learning causes Methods of reciprocating compressors based on consumers and manufacturers of RCs. The results showed that one of the main reasons for Machine learning is a subject that focuses on research of learning algorithms by compressor failure is valves, and it makes up 36%. Piston-cylinder units also constitute over which a machine can learn from the data nearly as well as people do [11]. Up to now, there 30% of all faults, where the failures of rings account for 25%. Failures in the slide-crank are a lot of machine learning methods that have been applied in RC fault diagnosis. In this mechanism and cranking mechanism are also significant [10]. To monitor compressor section,conditions, the four many most kinds prevalent of sensors algorithms were used in in machine fault detection learning systems, are reviewed. such as The vibration artifi- cialsensors, neural temperature network, support sensor, pressure vector mach sensor,ine, displacement and Bayesian sensor, network acoustic are emissionthree