Abstract
In recent years, many motor fault diagnosis methods have been proposed by analyzing vibration, sound, electrical signals, etc. To detect motor fault without additional sensors, in this study, we developed a fault diagnosis methodology using the signals from a motor servo driver. Based on the servo driver signals, the demagnetization fault diagnosis of permanent magnet synchronous motors (PMSMs) was implemented using an autoencoder and K-means algorithm. In this study, the PMSM demagnetization fault diagnosis was performed in three states: normal, mild demagnetization fault, and severe demagnetization fault. The experimental results indicate that the proposed method can achieve 96% accuracy to reveal the demagnetization of PMSMs.
Highlights
Permanent magnet synchronous motors (PMSMs) are widely used for consumer products and in industry
Ishikawa [1] proposed a demagnetization fault diagnosis method for PMSMs based on vibration signals, which were analyzed using a fast Fourier transform (FFT); the demagnetization situation was determined by comparing the difference in the frequency and amplitude between normal and demagnetization motors
Five different physical signals were captured directly from the motor driver as training data and test data as opposed to the method used in the literature, [4] which only adopts a stator current signal
Summary
Permanent magnet synchronous motors (PMSMs) are widely used for consumer products and in industry. The heat may lead to irreversible demagnetization and degrade the performance of PMSMs. For the purpose of predictive maintenance, several studies on demagnetization fault diagnosis of PMSMs were performed; signal analysis and intelligent learning algorithm are common methods. Ishikawa [1] proposed a demagnetization fault diagnosis method for PMSMs based on vibration signals, which were analyzed using a fast Fourier transform (FFT); the demagnetization situation was determined by comparing the difference in the frequency and amplitude between normal and demagnetization motors. Many physical signals can be used for fault diagnosis [1,2,3,4,5,6], but the additional installed sensors increase the cost. The demagnetization fault diagnosis using stator current signal analysis is popular as it does not require installation of additional sensors [7]
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