Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review

Leukel, Joerg; González, Julián; Riekert, Martin

doi:10.1016/j.jmsy.2021.08.012

Cited by 91 publications

(40 citation statements)

References 60 publications

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“…Data preprocessing is a crucial task in machine learning pipelines (Leukel, González, & Riekert, 2021). Here, data preprocessing denotes the process of preparing raw data for the machine learning model including data cleaning, signal selection, resampling, missing values treatment and data normalization.…”

Section: Preprocessingmentioning

confidence: 99%

Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

et al. 2022

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Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated and complex, multivariate relationships are neglected. In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge. We identify system failures using a threshold on the total reconstruction error (autoencoder reconstruction error including all sensor signals). For fault localization, we compute the individual reconstruction error (autoencoder reconstruction error for each sensor signal) allowing us to identify the signals that contribute most to the total reconstruction error. Expert knowledge is provided via look-up table enabling root-cause analysis and assignment to the affected subsystem. We demonstrated our findings in a cooling system unit including 34 sensors over a 8-months’ time period using 4-fold cross validation approaches and automatically created labels based on thresholds provided by domain experts. Using 4-fold cross validation, we reached a F1-score of 0.56, whereas the autoencoder results showed a higher consistency score (CS of 0.92) compared to the automatically created labels (CS of 0.62) – indicating that theanomaly is recognized in a very stable manner. The automatically created labels, however, detected anomaly earlier. The main anomaly was found by the autoencoder and automatically created labels, and was also recorded in the log files. Further, the explained fault localization highlighted the most affected component for the main anomaly in a very consistent manner.

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Section: Preprocessingmentioning

confidence: 99%

Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

et al. 2022

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“…As there are not many examples of anomaly detection for pitch systems, this section will examine anomaly detection for other wind turbine components. Machine learning is also utilised outside of wind energy, with a review of these practices being presented by J. Leukel et al [27], which examined articles that reported the use of Machine LEarning for predicting failures only using real world data. Of the systems examined, some were wind turbines.…”

Section: Anomaly Detection For Wind Turbinesmentioning

confidence: 99%

“…SCADA alarms, or fault information, for the turbines examined could provide contextual data to the results of the model in the future. The authors of [27] state that a future work should consider the effect of feature selection on machine learning techniques, as this has not adequately been investigated in the past.…”

mentioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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“…Several studies have been conducted to detect failures in industrial machines [ 14 ]. For example, deep-learning-based anomaly detection, a new detection method in another area of signal processing [ 15 , 16 ], can be used to detect such failures.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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One of the most important strategies for preventative factory maintenance is anomaly detection without the need for dedicated sensors for each industrial unit. The implementation of sound-data-based anomaly detection is an unduly complicated process since factory-collected sound data are frequently corrupted and affected by ordinary production noises. The use of acoustic methods to detect the irregularities in systems has a long history. Unfortunately, limited reference to the implementation of the acoustic approach could be found in the failure detection of industrial machines. This paper presents a systematic review of acoustic approaches in mechanical failure detection in terms of recent implementations and structural extensions. The 52 articles are selected from IEEEXplore, Science Direct and Springer Link databases following the PRISMA methodology for performing systematic literature reviews. The study identifies the research gaps while considering the potential in responding to the challenges of the mechanical failure detection of industrial machines. The results of this study reveal that the use of acoustic emission is still dominant in the research community. In addition, based on the 52 selected articles, research that discusses failure detection in noisy conditions is still very limited and shows that it will still be a challenge in the future.

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Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review

Cited by 91 publications

References 60 publications

Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

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