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Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

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Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis processes Article Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis Marco S. Reis 1,* and Geert Gins 2 1 CIEPQPF-Department of Chemical Engineering, University of Coimbra Polo II, Rua Sílvio Lima 3030-790, Coimbra, Portugal 2 AIXIAL Belgium, Charleroise Steenweg 112, B-1060 Brussels, Belgium; [email protected] * Correspondence: [email protected]; Tel.: +351-239-798-727 Academic Editors: Leo H. Chiang and Richard D. Braatz Received: 1 June 2017; Accepted: 27 June 2017; Published: 30 June 2017 Abstract: We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos. Keywords: industrial process monitoring; fault detection and diagnosis; prognosis; process health; equipment health 1. Introduction: Old and New Trends in Industrial Process Monitoring With the emergence of Industry 4.0 and the Big Data movement gaining momentum, industry is now presented with unique opportunities in terms of key enablers for boosting its performance to a new level. Performance is here taken in the widest sense, from operational, economic and market-related aspects to process safety and environmental. The key enablers are [1]: (i) data; (ii) technology and (iii) analytics (Figure1). In fact, data abounds now more than ever, and the speed at which they accumulate is accelerating: according to IBM, 1.6 zetabytes (1021 bytes) of digital data are now available, and this number is increasing [2]. This data deluge is possible because of the development of better, faster and more informative sensing technology, able to collect information from multiple sources, in order to store it in integrated databases and to make it available anywhere at any time. Technology also provides the computational resources (high performance computing, cloud services, distributed and parallel computing, etc.) required to process large amounts of data using advanced analytics platforms (the third enabler), turning them into actionable information, in useful time. The pressure to take advantage of the key enablers in every function and at any organizational level is rapidly building up [3]. Hitherto, this has happened more visibly in large companies [4], but small and medium enterprises may—and should—also engage in this endeavour [5]. The capacity of organizations to learn and adapt is now under test, and the race for turning the three key enablers into effective sources of competitive advantage is on. Processes 2017, 5, 35; doi:10.3390/pr5030035 www.mdpi.com/journal/processes Processes 2017, 5, 35 2 of 16 Processes 2017, 5, 35 2 of 16 FigureFigure 1. 1. TheThe key key enablers enablers underlying underlying the the Big Big Data Data movement. movement. AsAs most most core core functions inin anan industrial industrial enterprise, enterprise, Process Process Monitoring Monitoring must must inevitably inevitably follow follow this thispath path and and address address the the challenges challenges of synergisticallyof synergistically combining combining the the triplet triplet data/technology/analytics, data/technology/analytics, withwith all all knowledge knowledge and and practices practices developed developed and and acquired acquired over over almost almost 100 100 years, years, since since the the pioneering pioneering workwork of of Walter Walter A. A. Shewhart Shewhart in in the the early early 1920s 1920s [6]. [6]. Industrial Industrial Process Process Monitoring Monitoring (IPM) (IPM) is is an an activity activity ofof central central importance importance in companies in companies around around the world, the world, allowing allowing them to them achieve to higher achieve levels higher of safety, levels efficiency,of safety, quality, efficiency, profitability quality, and profitability environmental and environmental management performances management [7–9]. performances A retrospective [7–9]. analysisA retrospective of the evolution analysis of of the IPM evolution since its of IPMintroduction since its introduction clearly demonstrates clearly demonstrates the constant the struggle constant undertakenstruggle undertaken to adapt to to new adapt and to more new demanding and more demanding application application scenarios, characterized scenarios, characterized by harder‐to by‐ handleharder-to-handle data structures data arising structures from arising increasingly from increasingly complex processes. complex processes.In the next In subsections, the next subsections, some of thesome easy of (and the easy not so (and easy) not identifiable so easy) identifiable trends of the trends 10 decades of the 10 of decades existence of of existence IPM are of shortly IPM are referred, shortly includingreferred, includingthe more recent the more ones recent that may ones guide that may the guideevolution the evolutionof IPM in ofthe IPM near in future. the near future. 1.1.1.1. From From Univariate, Univariate, to to Multivariate, Multivariate, to to High High-Dimensional‐Dimensional (“ (“Mega-Variate”)Mega‐Variate”) AsAs a a response response to to the the increasing increasing availability availability of of sensors sensors and and data data acquisition acquisition systems systems collecting collecting informationinformation from from process process units units and and streams streams (e.g., (e.g., temperature, temperature, flow flow rate, rate, pressure, pressure, pH, pH, conductivity, conductivity, etc.),etc.), the the initially initially developed developed univariate univariate approaches approaches [6,10,11] [6,10,11 ]quickly quickly evolve evolve to to multivariate multivariate methodologiesmethodologies [12,13] [12,13 and] and then then to high to high-dimensional‐dimensional frameworks frameworks [14–18], [14– 18able], ableto cope to not cope only not with only thewith size, the but size, also but with also the with highly the collinear highly collinear (quite often (quite also often rank‐ alsodeficient) rank-deficient) nature of typical nature data of typical‐rich scenarios.data-rich This scenarios. is an old This and is well an old‐established and well-established trend of IPM, trend which of requires IPM, which no further requires introduction, no further sinceintroduction, it has been since widely it has addressed been widely and addressed discussed and in research discussed and in review research articles and review over the articles last 20 over years the [8,9,14,15,18–22].last 20 years [8,9 ,14,15,18–22]. 1.2.1.2. From From Homogeneous Homogeneous Data Data Tables Tables to to Heterogeneous Heterogeneous Datasets Datasets TheThe development development of of metrology metrology and and sensing sensing technology technology led led to to new new types types of of “variables” “variables” to to be be handled,handled, such such as as spectra, spectra, hyperspectral hyperspectral images, images, hyphenated hyphenated data, data, chromatograms, chromatograms, granulometric granulometric curves,curves, particle particle size size distributions, distributions, profilometric profilometric data, data, etc. etc. [23–27]. [23–27]. This This already already motivated motivated the the developmentdevelopment of of dedicated dedicated solutions solutions and and the the emergence emergence of Profile of Profile Monitoring Monitoring as a new as a field new in field IPM in [24,25,28,29].IPM [24,25,28 The,29]. homogeneous The homogeneous data datasources sources prevailing prevailing in most in most of the of the history history of of IPM, IPM, which which were were composedcomposed mainly mainly of processprocess sensors sensors and and univariate univariate quality quality measurements, measurements, all of all them of them collecting collecting scalar scalarvalues values at each at sampling each sampling time (the time so (the called so calledscalar sensorsscalar sensors), are now), are being now upgradedbeing upgraded with a with rich varietya rich varietyof data of structures, data structures, consisting consisting of higher of higher order order tensors, tensors, such such as spectra as spectra (1st order(1st order tensors), tensors), grey-level grey‐ level images (2nd order tensors), hyperspectral images (3rd order tensors), hyphenated Processes 2017, 5, 35 3 of 16 images (2nd order tensors), hyperspectral images (3rd order tensors), hyphenated measurements (nth order tensors, with n ≥ 2, known as tensorial sensors). This heterogeneous character of current industrial data is just the reflex of the Variety dimension of Big Data, in the scope of IPM. 1.3. From Static, to Dynamic, to Non-Stationary The inertial characteristics of industrial phenomena associated with the high sampling rates provided by modern instrumentation lead to the appearance of autocorrelation patterns in the collected data. The traditional
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