An identification method for mechanical fault diagnosis based on generalized matrix norm sparse filtering

2021 ◽  
Author(s):  
Zhao ting Shi ◽  
Jinrui Wang ◽  
Huaiqian Bao ◽  
Guifang Liu ◽  
Guowei Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Matrix Norm ◽  
Identification Method ◽  
Sparse Filtering ◽  
Generalized Matrix

scholarly journals A Non-Contact Fault Diagnosis Method for Bearings and Gears Based on Generalized Matrix Norm Sparse Filtering

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1075
Author(s):  
Huaiqian Bao ◽  
Zhaoting Shi ◽  
Jinrui Wang ◽  
Zongzhen Zhang ◽  
Guowei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Engineering Application ◽  
Planetary Gear ◽  
Acoustic Signals ◽  
Matrix Norm ◽  
Mechanical Equipment ◽  
Data Set ◽  
Sparse Filtering ◽  
Generalized Matrix ◽  
Diagnosis Method

Fault diagnosis of mechanical equipment is mainly based on the contact measurement and analysis of vibration signals. In some special working conditions, the non-contact fault diagnosis method represented by the measurement of acoustic signals can make up for the lack of contact testing. However, its engineering application value is greatly restricted due to the low signal-to-noise ratio (SNR) of the acoustic signal. To solve this deficiency, a novel fault diagnosis method based on the generalized matrix norm sparse filtering (GMNSF) is proposed in this paper. Specially, the generalized matrix norm is introduced into the sparse filtering to seek the optimal sparse feature distribution to overcome the defect of low SNR of acoustic signals. Firstly, the collected acoustic signals are randomly overlapped to form the sample fragment data set. Then, three constraints are imposed on the multi-period data set by the GMNSF model to extract the sparse features in the sample. Finally, softmax is used to as a classifier to categorize different fault types. The diagnostic performance of the proposed method is verified by the bearing and planetary gear datasets. Results show that the GMNSF model has good feature extraction ability performance and anti-noise ability than other traditional methods.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

A novel analysis method for fault diagnosis of hydro-turbine governing system

2017 ◽  
Vol 231 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Xin Xia ◽  
Wei Ni ◽  
Yingjun Sang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Response ◽  
Experimental Studies ◽  
Response Functions ◽  
Output Frequency ◽  
Frequency Response Functions ◽  
Hydro Turbine ◽  
Identification Method ◽  
Governing System ◽  
Diagnosis Method

The fault diagnosis of hydro-turbine governing system is important to the operation of the hydropower station and the stability of the power grid. In order to improve the diagnostic accuracy and efficiency, a novel fault diagnosis method based on nonlinear output frequency response functions and a novel identification method of nonlinear output frequency response functions have been proposed and applied to the problem of hydro-turbine governing system fault diagnostics. First, the nonlinear model of hydro-turbine governing system is built. And the fault diagnosis principles based on nonlinear output frequency response functions are also introduced. Then, the disadvantages of the traditional identification method are discussed, and a novel identification method is proposed for nonlinear output frequency response functions according to the operation characteristic of hydro-turbine governing system. Finally, simulation verification and experimental studies have been presented to demonstrate the accuracy and efficiency of the proposed fault diagnosis method. The results indicate that the proposed method is simple and practical for fault diagnosis of hydro-turbine governing system.

scholarly journals A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guowei Zhang ◽  
Jinrui Wang ◽  
Baokun Han ◽  
Sixiang Jia ◽  
Xiaoyu Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Planetary Gear ◽  
Acoustic Signals ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering ◽  
Third Stage ◽  
Learned Features

Increased attention has been paid to research on intelligent fault diagnosis under acoustic signals. However, the signal-to-noise ratio of acoustic signals is much lower than vibration signals, which increases the difficulty of signal denoising and feature extraction. To solve the above defect, a novel batch-normalized deep sparse filtering (DSF) method is proposed to diagnose the fault through the acoustic signals of rotating machinery. In the first stage, the collected acoustic signals are prenormalized to eliminate the adverse effects of singular samples, and then the normalized signal is transformed into frequency-domain signal through fast Fourier transform (FFT). In the second stage, the learned features are obtained by training batch-normalized DSF with frequency-domain signals, and then the features are fine-tuned by backpropagation (BP) algorithm. In the third stage, softmax regression is used as a classifier for heath condition recognition based on the fine-tuned features. Bearing and planetary gear datasets are used to validate the diagnostic performance of the proposed method. The results show that the proposed DSF model can extract more powerful features and less computing time than other traditional methods.

A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

2021 ◽  
pp. 095745652110557
Author(s):  
Lifeng Chan ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Propagation Algorithm ◽  
Sparse Filtering ◽  
Diagnosis Method

Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.

scholarly journals An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

2018 ◽  
Vol 20 (8) ◽  
pp. 2839-2854 ◽  
Author(s):  
Weiwei Qian ◽  
Shunming Li ◽  
Jinrui Wang ◽  
Zenghui An ◽  
Xingxing Jiang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering ◽  
Diagnosis Method
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