Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method

Yin, Jie; Xie, Yongfang; Chen, Zhiwen; Peng, Tao; Yang, Chunhua

doi:10.1007/s11771-019-4123-6

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…It is widely used in electric vehicles, rail transit, smart manufacturing, and other fields. During the running of motors, performance degradation and even failure will inevitably occur, which seriously affects the reliability and safety of the whole system [1,2]. The two most common faults in PMSMs are the inter-turn short circuit fault (ITSF) and the irreversible demagnetization fault (IDF) [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

et al. 2022

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In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the driving devices. Consequently, maintaining the symmetry of the system relies on the normal and stable operation of the PMSM, and it is necessary to diagnose faults in the PMSM in a timely manner. In PMSM fault diagnosis methods, frequency domain features of the stator current are extensively used. However, these features change with the switching of motor operating conditions, leading to difficulty of diagnosis in multiple operating conditions. Therefore, a fault diagnosis method based on a convolutional neural network (CNN) phase tracker is proposed in this paper. Through phase tracking and angular domain resampling, the fundamental frequency of stator currents in different operating conditions are aligned, so as to fix the distribution of frequency domain features and solve the problem of features changing with operating conditions. Experimental results show that the proposed method can resample the stator current signals with a small error, detect faults in a relatively short time with high accuracy, and diagnose fault type and severity level under multiple operating conditions.

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Section: Introductionmentioning

confidence: 99%

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

et al. 2022

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“…Wu et al [16] proposed a fault diagnosis algorithm for early failures based on the generalized estimation. Yin et al [17] applied a state resonance algorithm-based stochastic resonance method to high-speed train traction systems to achieve optimal extraction and frequency recovery. Cadini et al [18] used a particle filter to estimate the length of cracks to diagnose the early faults of aeronautical structure cracks.…”

Section: Introductionmentioning

confidence: 99%

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

Cheng

Wang

Luo

et al. 2020

Sensors

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As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

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Vibration signal-based early fault prognosis: Status quo and applications

Lv¹,

Zhao²,

Zhao³

et al. 2022

Advanced Engineering Informatics

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Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method

Cited by 6 publications

References 31 publications

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

Vibration signal-based early fault prognosis: Status quo and applications

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