Detecting Anomalous Behavior Towards Predictive Maintenance

Naskos, Athanasios; Gounaris, Anastasios; Metaxa, Ifigeneia N.; Köchling, Daniel

doi:10.1007/978-3-030-20948-3_7

Cited by 5 publications

(11 citation statements)

References 9 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%

“…The use of unsupervised techniques is motivated mostly by an absence of labeled data [47, 48, 50, 54, 59-62, 65, 70], although in some studies they are employed to detect outliers [74], reduce dimensionality [31,75] or extract features [81]. In studies [39,42,45,58,66,69,71], labeled data was available, but was used to validate the unsupervised learning models.…”

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

confidence: 99%

“…Only the studies presented in [55] and [69] proposed methods for detecting faults directly from real-time data and applied stream learning techniques.…”

Section: Rq5: Which Of Those Algorithms and Methods Are Used For Data...mentioning

confidence: 99%

“…Partition-based algorithms [69] Algorithm: appropriate for online learning; lowmemory and processing requirements; comprehensible results.…”

Section: Not Identifiedmentioning

confidence: 99%

“…In [69], MCOD was chosen because of its low-memory and processing requirements, which make it ideal for processing streaming data, as well as for producing results that are easy to understand. This algorithm has the downside of being sensitive to the input parameters, which can influence the number of outlier reports.…”

Section: Other Approachesmentioning

confidence: 99%

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

confidence: 99%

“…Only the studies presented in [55] and [69] proposed methods for detecting faults directly from real-time data and applied stream learning techniques.…”

Section: Rq5: Which Of Those Algorithms and Methods Are Used For Data...mentioning

confidence: 99%

“…Partition-based algorithms [69] Algorithm: appropriate for online learning; lowmemory and processing requirements; comprehensible results.…”

Section: Not Identifiedmentioning

confidence: 99%

Section: Other Approachesmentioning

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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A context-aware unsupervised predictive maintenance solution for fleet management

Giannoulidis

Gounaris

2022

J Intell Inf Syst

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We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.

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