Insitu Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System

Convolutional neural networks (CNNs) are one of the most efficient deep learning techniques and have been widely used in motor fault diagnosis. However, most of them are implemented in desktop computers to process offline signals. In this study, an insitu motor fault diagnosis method is proposed by implementing an enhanced CNN model into a designed embedded system consisting of a Raspberry Pi and a signal acquisition and processing circuit. To the best of our knowledge, this topic has not been investigated yet in the literature. First, the hardware, algorithms, and heterogeneous computing framework are introduced in detail. Then, the method effectiveness and efficiency are validated on a motor test rig. In particular, as the resources in an embedded system are limited, the algorithm accuracy and execution time are investigated. The robustness of the designed system is further validated by analyzing the motor signals with different signal-to-noise ratios. The contributions of this study include: 1) A heterogeneous computing framework is proposed and an integrated embedded system is designed. 2) The performance of the enhanced CNN in embedded system is validated. 3) The proposed method provides a solution to realize insitu motor fault diagnosis on a small-size, flexible, and convenient handheld device, by exploiting the artificial intelligence technique.