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
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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