Abstract
In this article, a type of diagnostic tool for an asynchronous motor powered from a frequency converter is proposed. An all-purpose, effective, and simple method for asynchronous motor monitoring is used. This method includes a single vibration measuring device fixed on the motor’s housing to detect faults such as worn-out or broken bearings, shaft misalignment, defective motor support, lost phase to the stator, and short circuit in one of the phase windings in the stator. The gathered vibration data are then standardized and continuous wavelet transform (CWT) is applied for feature extraction. Using morl wavelets, the algorithm is applied to all the datasets in the research and resulting scalograms are then fed to a complex deep convolutional neural network (CNN). Training and testing are done using separate datasets. The resulting model could successfully classify all the defects at an excellent rate and even separate mechanical faults from electrical ones. The best performing model achieved 97.53% accuracy.
Highlights
Nowadays, internet of things (IoT), industry 4.0, and other technologies maximize the performance and effectiveness of industry systems
Unsuccessful fault detection can cause drastic damage to some companies, and if not detected and fixed in time, a fault can lead to long stoppage of production lines
The features were so detailed because of advanced vibration sensors and feature extraction methods used, that it could distinguish 1 Ω difference in stator winding from vibrations
Summary
Internet of things (IoT), industry 4.0, and other technologies maximize the performance and effectiveness of industry systems. Unsuccessful fault detection can cause drastic damage to some companies, and if not detected and fixed in time, a fault can lead to long stoppage of production lines. This is especially important for automated and autonomous production, where there are minimal or no personnel on site. An autonomous fault prediction/detection system has been pursued in many articles and research for a long time indicating the need for such a system, and especially as electric machines evolve, those systems need to evolve . What makes a good, autonomous, effective, and cheap identification system from so many systems and models proposed?
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