An effective method for detection of stator fault in PMSM with 1D-LBP. (November 2020)

• Record Type: Journal Article
• Authors: Mi̇naz, Mehmet Recep
• Is Part Of: ISA transactions. Volume 106(2020)
• Page Start: 283
• Page End: 292
• Publication Date: 2020-11
• Subjects: Feature extraction -- 1D-LBP -- Fault detection -- Stator fault -- PMSM; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.07.013
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Permanent Magnet Synchronous Motors (PMSMs) have recently been used commonly in all areas of the industry due to their position control as well as precise speed. The success of these motors in applications of precise speed and position control depends on their whole operation. Even if the fault is at a highly-low-level, this negatively affects the precision of the motor. In this study, the one dimensional local binary patterns (1D-LBP) method, which is compelling and distinctive, has been used for feature extraction instead of frequency spectrum analysis or time–frequency analysis, which are among conventional feature extraction techniques in the literature, to detect short-circuit fault that occurs in PMSM stators. Thus, to test the proposed method, an experiment setup has been prepared to record current and voltage signals detected through 15 kHz sampling from healthy and faulty PMSM. 1D-LBP was applied to these current and voltage signals and the histograms of newly formed current and voltage signals were obtained. Histograms of newly formed signals are used as feature vectors. Healthy and faulty motors could be classified at high success rates applying one of the machine learning techniques, Knn algorithm, to histograms. It was found that the methods had a success rate over 90% when it was tested over-current and voltage data obtained from PMSM that ran at different speeds and loads and had different fault rates to test whether the methods ran properly. Highlights: One advantage is that this method uses all data points for feature extraction. It is fast and can be use in real-time application. High accuracies achieved for stator fault classification.