Vibration Analysis of Brushless Permanent Magnet Motor using Numerical Methods: Part I

Sharma, Vivek Kumar; Usman, Adil; Rajpurohit, Bharat Singh

doi:10.1109/iprecon49514.2020.9315220

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2022

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“…The Industrial Revolution gave way to multiple studies related to fault detection and monitoring of electrical machines, including BLDC motors [7] [12]. These studies are based on vibration signals to differentiate between healthy and faulty bearings using frequency components [13].…”

Section: Related Workmentioning

confidence: 99%

Cost-efficient real-time condition monitoring and fault diagnostics system for BLDC motor using IoT and Machine learning

Raja

Raval

Vaimann

et al. 2022

2022 International Conference on Diagnostics in Electrical Engineering (Diagnostika)

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A cost-efficient condition monitoring and fault diagnostic system are presented in this paper using the Internet of Things and machine learning. Most condition monitoring systems nowadays are either costly or used to monitor current values without emphasizing the analysis part. On the other hand, predictive maintenance of different electrical machines, including BLDC motors, is becoming the need of the hour. It reduces the cost needed for maintenance and can also be used to evade more significant faults in the machine. The data is transmitted in real-time using a data acquisition system onto the cloud, which is further processed to determine if there is a chance of any fault occurring in the motor. A short comparison of the results of different machine learning algorithms is also discussed related to predictive maintenance.

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Section: Related Workmentioning

confidence: 99%