Cloud based bearing fault diagnosis of induction motors

Year 2021, , 141 - 146, 20.10.2021

Aydil Bapir , İlhan Aydın

https://doi.org/10.53070/bbd.990814

Abstract

Abstract -- In general, induction motors predictive maintenance is well suited for small to large-scale industries to minimize failure, maximize performance, and improve reliability. The vibration of an induction motor was investigated in this paper in order to gather precise details that can be used to forecast motor bearing failure. With this in view, an induction motor carrying fault detection scheme has been attempted. machine learning algorithms in addition to wavelet transform (WT) and fast fourier transform (FFT), an advanced signal processing technique, are used in this study to analyze frame vibrations during initialization. the Internet of Things (IoT) is at the core of today's accelerated technological growth. A large number of items are interconnected efficiently, particularly in industrial-automation, resulting in condition and monitoring to boost efficiency to capture and process the parameters of induction motor, the proposed approach uses an IoT-based platform. The details gathered can be saved in the cloud platform and viewed via a web page.

Keywords

Induction motor, fault diagnosis, bearing faults, wavelet transform, Fast fourier transform.

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