Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems

摘要

Fault detection and diagnosis of induction motors in variable frequency drive (WD) applications is essential for minimizing unexpected downtime, material waste and equipment damage, ultimately contributing to sustainable manufacturing. This paper presents a multi-stream convolutional neural network (MS-CNN) for automatic feature extraction from and fusion of motor vibration and stator current at various line frequencies. The MS-CNN has demonstrated superior performance over conventional machine learning methods. To understand the rationale for MS-CNN to diagnose motor defects, the relevance of input features for fault classification by a trained MS-CNN are investigated through Layer-wise Relevance Propagation (LRP) of its predictions.