An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

2021 ◽  
Author(s):  
Chenhui Qian ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chunran Huo ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Bo Fang ◽  
Hu Jianzhong ◽  
Cheng Yang ◽  
Yudong Cao ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Automatic Differentiation ◽  
Blind Deconvolution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Convex Optimization Problem ◽  
Bearing Fault Diagnosis ◽  
Filter Performance ◽  
Calculation Process

Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

A Bearing Intelligent Fault Diagnosis Method Based on Cluster Analysis

2012 ◽  
Vol 152-154 ◽  
pp. 1628-1633 ◽  
Author(s):  
Su Qun Cao ◽  
Xiao Ming Zuo ◽  
Ai Xiang Tao ◽  
Jun Min Wang ◽  
Xiang Zhi Chen
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Test Data ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Type ◽  
Diagnosis Method

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Information Fusion of the Vibration and Acoustic Signals Based Rolling Bearing Incipient Fault Diagnosis Method

2013 ◽  
Vol 774-776 ◽  
pp. 1499-1502
Author(s):  
Ting Feng Ming ◽  
Yong Xiang Zhang ◽  
Jing Li
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Information Fusion ◽  
Vibration Signal ◽  
Acoustic Resonance ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Fusion Analysis

The feature of correlation analysis were described and applied to analyzing the vibration signal of the gearbox. Aiming to that the diagnosis effect of the rolling bearings incipient fault was not good through the vibration spectrum and the resonance demodulation spectrum directly, the information fusion technology based on the correlation analysis is proposed to processing the vibration and acoustic resonance demodulation signal. The experimental results show that the presented correlation fusion analysis technology can be as the basis of the effective fault diagnosis method for the rolling bearings incipient defect.

scholarly journals Application of modified culture Kalman filter in bearing fault diagnosis

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 757-765
Author(s):  
Wang Hailun ◽  
Alexander Martinez
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear Model ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Abstract Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.

scholarly journals A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1774
Author(s):  
Jun He ◽  
Ming Ouyang ◽  
Chen Yong ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Standard Deviation ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Intelligent Fault Diagnosis ◽  
Softmax Classifier ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Testing Accuracy ◽  
Intelligent Methods

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy

2010 ◽  
Vol 108-111 ◽  
pp. 1075-1079 ◽  
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Network Characteristic ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of neural network based on wavelet packet-characteristic entropy(WP-CE) the author proposes a new fault diagnosis method of vibrating of hearings, in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted, the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample the three layers BP neural network is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

scholarly journals Fault diagnosis method of rolling bearings Based on SPA-FE-IFSVM

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096947
Author(s):  
Hui Han ◽  
Lina Hao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Rolling Bearings ◽  
Data Set ◽  
Diagnosis Method

Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

2020 ◽  
Vol 72 (7) ◽  
pp. 947-953 ◽  
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Content Type ◽  
Noise Interference ◽  
Proposed Model ◽  
Long Time ◽  
Diagnosis Method

Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

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