Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines

• URL: http://arxiv.org/abs/2101.11539v1
• Date: Wed, 27 Jan 2021 16:49:49 GMT
• Title: Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines
• Authors: Sabtain Ahmad, Kevin Styp-Rekowski, Sasho Nedelkoski, Odej Kao
• Abstract summary: We propose an autoencoder model-based method for condition monitoring of rotating machines by using an anomaly detection approach. The proposed method can directly extract the salient features from raw vibration signals. Experimental results on two real-world datasets indicate that our proposed solution gives promising results.
• Score: 0.19116784879310028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rotating machines like engines, pumps, or turbines are ubiquitous in modern day societies. Their mechanical parts such as electrical engines, rotors, or bearings are the major components and any failure in them may result in their total shutdown. Anomaly detection in such critical systems is very important to monitor the system's health. As the requirement to obtain a dataset from rotating machines where all possible faults are explicitly labeled is difficult to satisfy, we propose a method that focuses on the normal behavior of the machine instead. We propose an autoencoder model-based method for condition monitoring of rotating machines by using an anomaly detection approach. The method learns the characteristics of a rotating machine using the normal vibration signals to model the healthy state of the machine. A threshold-based approach is then applied to the reconstruction error of unseen data, thus enabling the detection of unseen anomalies. The proposed method can directly extract the salient features from raw vibration signals and eliminate the need for manually engineered features. We demonstrate the effectiveness of the proposed method by employing two rotating machine datasets and the quality of the automatically learned features is compared with a set of handcrafted features by training an Isolation Forest model on either of these two sets. Experimental results on two real-world datasets indicate that our proposed solution gives promising results, achieving an average F1-score of 99.6%.

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