Low-Cost System for Investigating a Small Motor Fault Classification Based on Current Signal

This research presents low-cost system for motor fault classification. This system uses a microcontroller with built-in ADC and communication capability. The two purposes of this article are to investigate the quality of the system for data acquisition and capability of the system for detecting early motor faults both by microcontroller on the system and personal computer. The embedded software on the system is designed to record current signals from a current sensor, compute FFT-based features and classify the fault based on tinyML method. Data communication between the system and the personal computer can be done by both serial port and TCP socket over Wi-Fi. The performances of the system and the personal computer are compared by the experiment as well as the quality of data recorded from the built-in ADC and a digital oscilloscope. The broken rotor bar and bearing fault in a 2.2kW induction motor are investigated. The classifier used in the experiment is a small feed forward neural network which can be implemented on both the proposed low-cost system and the personal computer. Although the recorded electrical current data by built-in ADC is contaminated with noise, the fault classification on the personal computer yield accuracy up to 90%.