Non-invasive intelligent monitoring system for fault detection in induction motor based on lead-free-piezoelectric sensor using ANN

Abstract

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements were collected then monitored in real time and transmitted via an ESP32 board. A new, flexible, lead-free piezoelectric sensor, developed previously in our laboratory, was used for vibration analysis (VA). An infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network (ANN) algorithm implemented on the microcontroller. Additionally, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of VA, thermal signature analysis and ANN provides a better diagnosis and provides efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.