Motor PHM on Edge Computing with Anomaly Detection and Fault Severity Estimation through Compressed Data Using PCA and Autoencoder

Abstract

The motor is essential for manufacturing industries, but wear can cause unexpected failure. Predictive and health management (PHM) for motors is critical in manufacturing sites. In particular, data-driven PHM using deep learning methods has gained popularity because it reduces the need for domain expertise. However, the massive amount of data poses challenges to traditional cloud-based PHM, making edge computing a promising solution. This study proposes a novel approach to motor PHM in edge devices. Our approach integrates principal component analysis (PCA) and an autoencoder (AE) encoder achieving effective data compression while preserving fault detection and severity estimation integrity. The compressed data is visualized using t-SNE, and its ability to retain information is assessed through clustering performance metrics. The proposed method is tested on a custom-made experimental platform dataset, demonstrating robustness across various fault scenarios and providing valuable insights for practical applications in manufacturing.