A Non-Intrusive Multi-Parameter Fault Diagnosis System for Industrial Machineries

Induction motor, especially driving motor, is the critical component for various modern industrial machineries. Fault diagnosis of induction motor is therefore a necessary and crucial task to ensure the machinery health and prevent vital damages. Conventional diagnosis methods are mainly based on intrusive sensors to measure certain physical parameters. However, intrusive sensors are costly and hard to apply to update traditional machines. In this paper, we propose EMFD, an energy-image based non-intrusive multi-parameter fault diagnosis system. We design an EMFD sensing platform to monitor the electric circuit parameters. Then we build a fault model that describes the relationships between two major kinds of faults and the electric circuit parameters. Based on the model, we propose a novel fault diagnosis algorithm that exploits a sparse auto-encoder based deep neural network. Different from the existing single-parameter methods, EMFD takes advantage of multiple circuit parameters and achieves accurate and robust diagnosis even in dynamic operating environments. We implement and deploy the proposed system in a real-world factory. The evaluation results show that EMFD can achieve the diagnosis accuracy of 96 %.