Permanent magnet synchronous motors (PMSM) are widely used in industry applications such as home appliances, manufacturing process, high-speed trains, and electric vehicles. Unexpected faults of PMSM are directly related to the significant losses in the engineered systems. The majority of motor faults are bearing fault (mechanical) and stator fault (electrical). This article reports vibration and driving current dataset of three-phase PMSM with three different motor powers under eight different severities of stator fault. PMSM conditions including normal, inter-coil short circuit fault, and inter-turn short circuit fault in three motors are demonstrated with different powers of 1.0 kW, 1.5 kW and 3.0 kW, respectively. The PMSMs are operated under the same torque load condition and rotating speed. Dataset is acquired using one integrated electronics piezo-electric (IEPE) based accelerometer and three current transformers (CT) with National Instruments (NI) data acquisition (DAQ) board under international organization for standardization standard (ISO 10816-1:1995). Established dataset can be used to verify newly developed state-of-the-art methods for PMSM stator fault diagnosis. Mendeley Data. DOI: 10.17632/rgn5brrgrn.5.
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http://dx.doi.org/10.1016/j.dib.2023.108952 | DOI Listing |
Sensors (Basel)
November 2024
Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China.
Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features.
View Article and Find Full Text PDFData Brief
April 2024
Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, Ontario, Canada.
Induction motors are used in industry as they are self-starting, reliable, and affordable. Applications for these motors include lathes, mills, pumps, power conveyor belts, and commercial electrical and hybrid vehicles. Induction motors have various types of failures, including rotor unbalance, rotor misalignment, stator winding faults, voltage unbalance, bowed rotor, broken rotor bars, and faulty bearings.
View Article and Find Full Text PDFSci Rep
July 2024
Department of Electrical Engineering, Yildiz Technical University, Istanbul, 34220, Türkiye.
In addition to research related to static eccentricity and dynamic eccentricity examining fault and diagnostic methods in various types of motors, there are also studies conducted for motors with non-uniform air gaps similar to the condition. In these studies, improvements and changes in motor performance for such types of motors can be observed. In this study, the effects of the rotor structures determined in the study and the air gap shape, which is effective by changing the eccentricity ratio, on the motor performance of the interior permanent magnet synchronous motor designed for electric vehicle applications are examined.
View Article and Find Full Text PDFSensors (Basel)
April 2024
Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Zhejiang Academy of Special Equipment Science, Hangzhou 310020, China.
The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper.
View Article and Find Full Text PDFEntropy (Basel)
March 2024
Digital Systems Group, National Institute of Astrophysics, Optics and Electronics, Puebla 72840, Mexico.
In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations.
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