Anomaly Detection and Fault Classification in Multivariate Time Series Using Multimodal Deep Models
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Abstract
In the realm of gear fault diagnosis, where various analytical methods often require extensive domain expertise, automation remains challenging due to diverse fault diagnosis tasks. To address these limitations, we propose a novel PHM algorithm integrating out-of-distribution detection and representation learning. Initial steps involve feature extraction using envelopes and fast Fourier transform (FFT). Representation Learning employs Transformers and Self-supervised learning for meaningful representations. The latent space values are then utilized for Out-of-Distribution Detection through kNN and classification, achieving a remarkable 99% accuracy. Our approach significantly enhances gear fault diagnosis automation, proving effective across diverse, unencountered problems.
How to Cite
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Anomaly, Fault Detection, Deep Models
Lu, C., Wang, Z.-Y., Qin, W.-L., & Ma, J. (2017). Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 130, 377–388. doi:10.1016/j.sigpro.2016.07.028
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021, August). A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 2114-2124).
Sun, Y., Ming, Y., Zhu, X., & Li, Y. (2022, June). Out-of-distribution detection with deep nearest neighbors. In International Conference on Machine Learning (pp. 20827-20840). PMLR.
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