Intelligent Fault Prediction for Electromechanical Equipment: A Hybrid LSTM-XGBoost Approach

Electromechanical equipment plays a pivotal role in modern industrial systems, powering critical operations in manufacturing, energy generation, and transportation. Unexpected equipment failures can lead to catastrophic consequences, including production downtime, safety hazards, and costly repairs. Traditional maintenance strategies, such as reactive or scheduled maintenance, often fail to address these challenges due to their inherent inefficiency and high operational costs. Predictive maintenance, enabled by intelligent fault prediction systems, offers a transformative solution by leveraging real-time sensor data and historical performance metrics to anticipate potential failures before they occur. Such systems not only minimize unplanned downtime but also optimize resource allocation, reduce maintenance expenditures, and extend equipment lifespan. The degradation of electromechanical equipment can be attributed to two distinct categories of factors. Macro-level determinants, encompassing intrinsic characteristics such as manufacturing quality, design robustness, and cumulative operational stress and micro-level environmental influences, including real-time ambient conditions within industrial facilities, such as temperature fluctuations, humidity levels, and vibration exposure. In this paper, we propose a hybrid analytical framework that synergistically integrates the temporal dependency modeling capabilities of Long Short-Term Memory (LSTM) networks with XGBoost’s proficiency in handling high-dimensional heterogeneous features. This LSTM-XGBoost architecture is specifically designed to holistically address both macro-level determinants and microlevel environmental influences in electromechanical systems.