Hybrid fault diagnostics and prognostics system development for motor bearings health monitoring

The article focuses on developing a specialized prognostics system for motor bearings to ensure the safety and reliability of motors and generators. Timely maintenance improves both equipment health and economic viability. The primary objective is to predict early-stage faults, anticipate degradation, and analyze the Remaining Useful Life (RUL) without interrupting the system. The versatile system is developed to continuously monitors deviation profiles, calculates RUL, and optimizes the industrial performance. To address the persistent challenge of imprecise model, integrating sensor data and understanding the physical context, the proposed framework combines data-driven and model-driven learning approaches. The hybrid diagnostics and prognostics system employ Convolutional-LSTM (CNN-LSTM) for feature extraction and fault diagnostics. Particle Filtering (PF) is integrated for stochastic degradation state prediction and RUL estimation. The framework prioritizes learning degradation trends from sensor data without direct physical integration. To validate the effectiveness of the proposed scheme, experiments were performed using the popular PROGNOSTIA bearings dataset, confirming its superior performance in RUL prediction. The developed prognostics system will find broad applicability in process monitoring across various industries, advancing the field and optimizing maintenance practices.