A generic fault prognostics algorithm for manufacturing industries using unsupervised machine learning classifiers

Kolokas, Nikolaos; Vafeiadis, Thanasis; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

doi:10.1016/j.simpat.2020.102109

Cited by 29 publications

(21 citation statements)

References 34 publications

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“…As can be seen in Fig. 6, 53.3% of publications employ supervised learning techniques, 28.9% use unsupervised learning techniques, 15.6% make use of both supervised and unsupervised techniques and 2.2% [56] conference Advances in Manufacturing [57] journal Applied Sciences [58] journal Business & Information Systems Engineering [59] journal Complexity [60] journal Computers & Industrial Engineering [61] journal Electronics [62] journal Engineering Applications of Artificial Intelligence [63] journal Expert Systems with Applications [64] journal IEEE Transactions on Industrial Electronics [31] journal IEEE Transactions on Industrial Informatics [65] journal Journal of Manufacturing Systems [66] journal Simulation Modelling Practice and Theory [67] journal Studies in Informatics and Control [68] journal 2019 31st International Conference on Advanced Information Systems Engineering (CAiSE) [69,70] conference CIRP Annals [71,72] journal Sensors [73,74] journal The International Journal of Advanced Manufacturing Technology [75,76] journal IEEE Access [77][78][79][80][81] journal Fig. 4 Proportion of publications in conferences and journals combine semi-supervised, unsupervised, and supervised techniques.…”

Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

“…Instance-based algorithms K-NN [39] supervised Latent Variable Models PCA [65] unsupervised GMM [47] unsupervised K-Means [54] unsupervised PLSR [64] supervised K-SVD [60] unsupervised K-MDTSC [62] unsupervised Artificial Neural Networks ANN [57] supervised BPNN [40] supervised CNN [78] supervised DNN [77] supervised LSTM [70] supervised MLP [56] supervised SSAE + BPNN [31] unsupervised + supervised SSAE + Softmax Classifier [81] unsupervised + supervised LSTM Autoencoder [73] supervised LSTM -GAN [79] supervised RNN [55] supervised Conditional Variational Autoencoder [66] unsupervised Rule-based models R4RE ("Rules 4 Rare Events" based on QARMA) [49] supervised XCSR [51] supervised consists in the principal components obtained from the application of DPCA, which do not represent any physical properties or measurements of the system. The study presented in [67] used an ensemble method as well due to its efficiency in terms of computation time and memory when handling large amounts of data.…”

Section: Decision Treesmentioning

confidence: 99%

“…[53] Algorithm: efficiency; effectiveness. Not identified [67] Algorithm: computational efficiency Not identified [71] Algorithm: handles bias and variance issues Proposed approach: theoretical guarantee for prognostic performance; captures complex fault patterns and fault propagation pathways.…”

Section: Publicationmentioning

confidence: 99%

“…Tree ensembles were also used for survival analysis [71] and anomaly detection [67]. In 2019, the study presented in [71] explored the rich data provided by the plant floor automation and information system (PFS) of a realworld automotive manufacturing line to learn complex and dynamic machine breakdown patterns.…”

Section: Decision Treesmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

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Section: Rq3: What Machine Learning Algorithms and Methods Are Curren...mentioning

confidence: 99%

Section: Decision Treesmentioning

confidence: 99%

Section: Publicationmentioning

confidence: 99%

Section: Decision Treesmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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“…An adaptive health assessment could be used to diagnose system reliability and forecast the machine condition, or a system based on health monitoring information. In [ 38 ], a generic methodology based on machine learning methods to strongly correlate faults detected in historical data of process log with the upcoming data stream, according to a prediction scope is presented. Data comes from the aluminum domain and represents the flow of the different phases and machine data comes from the process aluminum electrolysis and represents the flow of the different phases and machines to prepare the paste and form the anode (carbon blocks using for the aluminum reduction process).…”

Section: Case Study: Fault Diagnosismentioning

confidence: 99%

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

Hinojosa-Palafox

Rodríguez-Elias

Hoyo-Montaño

et al. 2021

Sensors

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The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

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Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

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A generic fault prognostics algorithm for manufacturing industries using unsupervised machine learning classifiers

Cited by 29 publications

References 34 publications

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

Data mining in predictive maintenance systems: A taxonomy and systematic review

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