Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM

Liang, Siyuan; Chen, Yong; Liang, Hong; Li, Xu

doi:10.3390/app9020224

Cited by 22 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hou et al used the matching pursuit (MP) algorithm [98] to obtain the largest N sparse coefficients of PMSM with different faults and considered them as the features [99]. Based on this, Liang et al used the orthogonal matching pursuit (OMP) algorithm [100] and sent the feature vectors into SVM [101]. As for classification, SRC is not as accurate as SVM, although it is faster.…”

Section: Sparse Representationmentioning

confidence: 99%

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

et al. 2019

Self Cite

View full text Add to dashboard Cite

Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

show abstract

Section: Sparse Representationmentioning

confidence: 99%

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the field of fault diagnosis, there have been many scholars and researchers who have carried out related works. Among them, some researchers have mainly used traditional machine learning methods, such as support vector machines (SVMs) and random forests [7,8], for fault diagnosis of mechanical devices such as electric motors. For example, Zhang and Zhou [9] proposed an electric motor fault diagnosis method based on signal decomposition and SVM, which combines empirical modal decomposition and optimized SVM to achieve the diagnosis of bearing faults.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention

Zeng,

Ren,

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Identification of motor vibration signals is one of the important tasks in motor fault diagnosis and predictive maintenance, and wavelet time-frequency diagram is a commonly used signal analysis method to extract the frequency and time characteristics of signals. In this paper, a method based on LSH-Swin Transformer network is proposed for identifying the wavelet time-frequency diagrams of motor vibration signals to analyze the fault types.The traditional Swin Transformer model is difficult to improve the accuracy due to the smoothing of the attention distribution when dealing with data with sparse features, while the method proposed in this paper reduces the smoothing of the computed attention and enables the network to learn the key features better by introducing locally-sensitive hash attention in the network model, dividing the sequences in the input attention into multiple hash buckets, calculating the attention weights of only some of the vectors with a high degree of hash similarity, and by sampling discrete samples with the use of the Gumbel Softmax. The experimental results show that the method proposed in this paper has better recognition accuracy and higher computational efficiency compared with the traditional network when processing wavelet time-frequency maps of motor vibration signals, and its validation accuracy reaches 99.7%, the number of parameters also has a decrease of about 10%, and the training network to reach converged epochs is also faster. The method in this paper can provide an effective solution for the analysis and processing of motor vibration signals, and has certain application value in practical engineering.

show abstract

“…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 also be used for fault diagnosis [1][2][3][4][5][6], but the additional installed sensors increase the cost. To reduce cost, the demagnetization fault diagnosis using stator current signal analysis is also popular as it does not require installation of additional sensors [7].…”

Section: Introductionmentioning

confidence: 99%

Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering

2020

View full text Add to dashboard Cite

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.

show abstract

Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM

Cited by 22 publications

References 17 publications

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention

Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering

Contact Info

Product

Resources

About