Research on Bearing Fault Identification of Wind Turbine Based on Deep Belief Network

Cheng, Shucan; Tao, Wenqiang; Zhao, Yanpu

doi:10.1109/ei250167.2020.9346678

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…SDFM [27] 1160 0 1090 0 DMDA [26] 1218 1 1150 1 SCNN [15] 1153 4 981 6 DBN [32] 1162 3 990 2 Method of this article 1050 0 900 0…”

Section: Number Of False Alarmsmentioning

confidence: 99%

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

Li¹,

Chen²,

Hou³

et al. 2022

Electronics

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For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual three-phase permanent magnet synchronous motors (PMSMs) with complex winding structures, and this kind of incipient fault detection is more complicated. To solve this detection difficulty, an IITSC detection method for dual three-phase PMSMs is proposed based on a modified deep autoencoder (MDAE). This autoencoder (AE) adopts an improved distribution metric combined with the maximum mean discrepancy (MMD) and the maximum covariance discrepancy (MCD) to extract the fault feature from the common features, which can improve the feature difference between the normal state and the incipient fault state. Then, the permutation entropy of the extracted features is calculated to detect the IITSC faults. The results illustrate that this method can not only detect IITSC faults online effectively and robustly, but also reduce the false alarm rate of the fault detection for dual three-phase PMSMs.

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“…SDFM [27] 1160 0 1090 0 DMDA [26] 1218 1 1150 1 SCNN [15] 1153 4 981 6 DBN [32] 1162 3 990 2 Method of this article 1050 0 900 0…”

Section: Number Of False Alarmsmentioning

confidence: 99%

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

Li¹,

Chen²,

Hou³

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…With the rapid development of deep learning, intelligent fault diagnosis has provided new ideas (Cheng et al, 2020; Liang et al, 2018). Convolutional neural networks (CNN), as the basic model of deep learning, can automatically extract the features of the original signal and reduce the reliance on experience, solving the problems of traditional methods that rely on expert experience, are time-consuming and have poor generalization ability (Shao et al, 2017; Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

Hou

2022

Wind Engineering

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A fault diagnosis method based on a multi-scale feature fusion network (MSFF-CNN) is proposed for the problem that the vibration signals of wind turbine bearings are easily disturbed by noise, and feature extraction is harrowing. Compared with the traditional diagnosis method, which has two stages of manual feature extraction and fault classification, this method combines the two into one. First, based on the characteristics of the bearing vibration signal, the multi-scale kernel algorithm is used to learn features in parallel at different scales. Then, the features extracted at different scales are fused to obtain complementary and rich diagnostic information. Finally, the Softmax classifier is used to output the fault diagnosis results. The simulation is carried out through the bearing vibration data of Case Western Reserve University. The results show that the accuracy of bearing fault diagnosis reaches 99.17%, proving the proposed method’s high accuracy and effectiveness.

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Research on Bearing Fault Identification of Wind Turbine Based on Deep Belief Network

Cited by 2 publications

References 3 publications

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

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