A Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

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Published Nov 5, 2024
Fabrizio De Fabritiis Konstantinos Gryllias

Abstract

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan.

In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications.

In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks.

The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

How to Cite

De Fabritiis, F., & Gryllias, K. (2024). A Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3911
Abstract 120 | PDF Downloads 113

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Keywords

Condition monitoring, Anomaly detection, Self learning, Rolling element bearings, Fault detection

References
Bi, Z., Yang, Y., Du, M., Yu, X., He, Q., & Peng, Z. (2024). Unsupervised hypersphere description approach for detecting and localizing anomalies in drivetrain with normal data. Measurement, 114349.

Bogue, R. (2013). Sensors for condition monitoring: A review of technologies and applications. Sensor Review, 33(4), 295–299.

Chen, Z., Mauricio, A., Li, W., & Gryllias, K. (2020). A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mechanical Systems and Signal Processing, 140, 106683.

Cui, Y., Bangalore, P., & Tjernberg, L. B. (2018). An anomaly detection approach using wavelet transform and artificial neural networks for condition monitoring of wind turbines’ gearboxes. In 2018 power systems computation conference (pscc) (pp. 1–7).

Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2019). Temporal cycle-consistency learning. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 1801–1810).

Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., & Tao, D. (2023). A survey on self-supervised learning: Algorithms, applications, and future trends. arXiv preprint arXiv:2301.05712.

Henriquez, P., Alonso, J. B., Ferrer, M. A., & Travieso, C. M. (2013). Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Transactions on systems, man, and cybernetics: Systems, 44(5), 642–652.

Jalayer, M., Orsenigo, C., & Vercellis, C. (2021). Fault detection and diagnosis for rotating machinery: A model based on convolutional lstm, fast fourier and continuous wavelet transforms. Computers in Industry, 125, 103378.

Kim, G., Han, D. K., & Ko, H. (2021). Specmix: A mixed sample data augmentation method for training withtime-frequency domain features. arXiv preprint arXiv:2108.03020.

Liu, C., & Gryllias, K. (2020, 01). A semi-supervised support vector data description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mechanical Systems and Signal Processing, 140.

Liu, C., & Gryllias, K. (2021). A deep support vector data description method for anomaly detection in helicopters. In Phm society european conference (Vol. 6, pp. 9–9).

Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019). Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779.

Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of sound and vibration, 289(4-5), 1066–1090.

Russell, M., & Wang, P. (2023). Maximizing model generalization for machine condition monitoring with selfsupervised learning and federated learning. Journal of Manufacturing Systems, 71, 274–285.

Yan, R., Dunnett, S., & Jackson, L. (2023). Impact of condition monitoring on the maintenance and economic viability of offshore wind turbines. Reliability Engineering & System Safety, 238, 109475.
Section
Technical Research Papers

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