Fault Diagnosis of Hydraulic Generator Bearing by VMD-Based Feature Extraction and Classification

Tang, Xuanrong; Hu, Baishi; Wen, He

doi:10.1007/s40998-021-00421-0

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Therefore, it is of great significance to study bearing fault diagnosis and gradually improve the diagnosis efficiency for maintaining the stable operation of gas-fired generators. In the bearing fault diagnosis based on vibration, temperature, and acoustic signals, vibration signals containing rich equipment operating status information are the most widely used [2]. Fault feature extraction and classification are two key steps of bearing fault diagnosis based on vibration signal [3].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Zhao

Zhang

et al. 2023

IEEE Access

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The main bearing is the core component of gas-fired generator, and its reliability directly affects the stability of the whole system. Therefore, it is of great significance to study the fault diagnosis of the main bearing of gas-fired generator. In the bearing fault diagnosis based on vibration signal, how to extract the signature features of fault effectively is the key to achieving accurate fault diagnosis. Based on extracting the signature features of faults, how to classify the fault features efficiently is another key to achieving accurate fault diagnosis. Based on this, we propose a bearing fault diagnosis method based on Mel frequency cepstrum coefficient (MFCC) and deformable space-frequency attention network (DSFAN). In view of the inconsistent feature distribution of different types of faults, the MFCC algorithm is introduced to preprocess the original fault signals and extract their signature features. Then, the network model DSFAN is constructed based on the space-frequency feature attention mechanism (SFFAM). DSFAN can extract the global constraint features and distributed constraint features of fault signals and realize bearing fault diagnosis. To make full use of classification information, the data processed by MFCC is constructed into a three-dimensional data cube as the input of DSFAN. Finally, the validity of the proposed method MFCC-DSFAN is verified on CWRU, XJTU, and gas-fired generator data sets. The experimental results show the excellent performance of MFCC-DSFAN for fault diagnosis and prove the effectiveness of the attention module in feature extraction.

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Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Zhao

Zhang

et al. 2023

IEEE Access

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“…21 Tang et al realized the fault diagnosis of hydraulic generator bearing and achieved high classification accuracy through the model of VMD-SVM. 22 These machine learning classification algorithms still have their own disadvantages to overcome. While the artificial neural network is capable of fault classification when processing a large amount of data, adjusting the network structure parameters can be timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Synchronous Generator Based on Adaptive Chirp Mode Decomposition Permutation Entropy and Deep Learning

Zhang,

Jin,

2023

The International Journal of Acoustics and Vibration

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Generators are subject to extreme environmental conditions that can cause critical areas to gradually break down, potentially leading to catastrophic failures. This paper proposes a hybrid method for generator fault diagnosis. Firstly, adaptive chirp mode decomposition (ACMD) is applied to decompose the vibration signal into five intrinsic mode function (IMF) components. Then, the permutation entropy (PE) of each IMF is calculated to construct the feature vector. The deep learning part of the proposed method uses convolutional neural network (CNN) as a classifier to recognize different faults. Finally, the visualization result using t-Distributed Stochastic Neighbor Embedding (t-SNE) is presented. The result of classification suggests that the method proposed in this paper realizes fault diagnosis with the accuracy of 98%, which has a higher recognition rate than other methods mentioned in this paper.

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“…The coupling effect of multiple sources creates huge difficulties and presents great challenges for research into vibration issues. The vibration prototype observation of the hydropower house structure based on the excitation of the working environment can obtain the required vibration parameters and response under the normal operation working conditions, but because the vibration signal of the hydropower house is a non-linear signal with varied noise, the information of the vibration characteristics is often drowned by the noise under the joint influence of multiple vibration sources, which affects the accuracy of the subsequent data analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Study on Vibration-Transmission-Path Identification Method for Hydropower Houses Based on CEEMDAN-SVD-TE

Zhang

Huang

et al. 2022

Applied Sciences

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The analysis of the vibration-transmission path is one of the keys to the vibration control and safety monitoring of a hydropower house, and the vibration source of the hydropower house is complex, making it more difficult to analyze the vibration-transmission path. In order to accurately identify the transmission path of the vibration in a hydropower house, an identification method for the vibration-transmission path based on CEEMDAN-SVD-TE is presented in this paper. First of all, this paper verifies that the CEEMDAN-SVD-TE method has higher effectiveness and is superior to the single transfer-entropy (TE) algorithm in information-transmission-direction identification; secondly, based on the measured field-vibration data, CEEMDAN-SVD noise-reduction technology is used to adaptively decompose the characteristics according to the signal energy; finally, the transfer-entropy theory and the information-transmission rate are used to determine the vibration-transmission path of the hydropower house. The results show that the main transmission path of the vibration caused by tailwater fluctuation is tailwater pipe (top cover measurement point)→turbine pier (stator foundation measurement point, lower frame foundation measurement point)→generator floor (generator floor measurement point). This research can offer a reference for vibration control and safety monitoring of hydropower houses, and provide a new idea for structural vibration reduction.

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Fault Diagnosis of Hydraulic Generator Bearing by VMD-Based Feature Extraction and Classification

Cited by 12 publications

References 34 publications

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Fault Diagnosis of Synchronous Generator Based on Adaptive Chirp Mode Decomposition Permutation Entropy and Deep Learning

Study on Vibration-Transmission-Path Identification Method for Hydropower Houses Based on CEEMDAN-SVD-TE

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