Anomaly Detection and Fault Classification in Multivariate Time Series Using Multimodal Deep Models

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.