Non-invasive intelligent monitoring system for fault detection in induction motor based on lead-free-piezoelectric sensor using ANN. (1st June 2022)

• Record Type: Journal Article
• Authors: Gana, Massine; Achour, Hakim; Belaid, Kamel; Chelli, Zakia; Laghrouche, Mourad; Chaouchi, Ahcène
• Is Part Of: Measurement science & technology. Volume 33:Number 6(2022)
• Publication Date: 2022-06-01
• Subjects: artificial neural networks -- vibration analysis -- piezoelectric sensor -- thermal analysis -- internet of things; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac4b8f
• Languages: English
• ISSNs: 0957-0233

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.