Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of Things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

I. Introduction

All seem highly concerned about and value resource conservation and carbon reduction; thus, the electric vehicle (EV) industry is rapidly flooding the market [1], [2]. The pivotal components of an EV are the drive motors, among which induction motors (IMs) are the most widely used [3]. The motor rotor, as the key component in an IM, considerably affects the performance and efficiency of the IM [4]. Die casting is a mainstream production process of IM rotors. As the die cast rotor goes through a complex manufacturing process, multiple defects could occur/emerge in the rotor bars during production, such as porosity and destruction [5], which will result in low yield and must be detected in a timely manner. Thus, methods for an accurate evaluation of rotor quality are necessary, and considerable research has been devoted to the detection of rotor faults [6].