scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

Generative Adversarial Network and Transfer Learning Based Fault Detection for Rotating Machinery with Imbalance Data Condition

2021 ◽  
Author(s):  
jun li ◽  
Yongbao Liu ◽  
Qijie Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Learning Method ◽  
Intelligent Fault Diagnosis ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Proposed Model ◽  
Diagnosis Method

Abstract Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of the outstanding data-driven capability. However, the severe imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis method. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning method based on the Time-GAN and Efficient-Net models. Firstly, the proposed model so called Time-GAN-TL extends the imbalanced fault diagnosis of rolling bearings by using time series generative adversarial network. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the Efficient-Net into the transfer learning method. Finally, the proposed method is validated using two-types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.

scholarly journals Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

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.

The Bearing Vibration Signal Collecting System Based on STM32F103C8T6

2014 ◽  
Vol 971-973 ◽  
pp. 1376-1379
Author(s):  
Zhong Hu Yuan ◽  
Man Yang Xu ◽  
Xiao Xuan Qi
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bearing Fault Diagnosis ◽  
Communication Module ◽  
Bearing Vibration ◽  
Signal Conditioning Circuit ◽  
Collecting System ◽  
Conditioning Circuit ◽  
New System

Vibration signal collecting is an important step in rolling bearing fault diagnosis process. The collected signal can exhibit effectiveness of the fault depend on the signal collecting system. Combine the attribute of the rolling bearing, a new signal collecting system base on the STM32F103C8T6 is designed in this paper. The new system is made of supply circuit, signal conditioning circuit, AD conversion circuit and communication module.

High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

2013 ◽  
Vol 753-755 ◽  
pp. 2290-2296 ◽  
Author(s):  
Wen Tao Huang ◽  
Yin Feng Liu ◽  
Pei Lu Niu ◽  
Wei Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Frequency Resolution ◽  
Sparse Decomposition ◽  
High Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Frequency ◽  
Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

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.

scholarly journals Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Tao ◽  
Yilun Liu ◽  
Dalian Yang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Identification Accuracy ◽  
Learning Ability ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
The Individual

In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

Rolling Bearing Fault Diagnosis Based on Second Generation Wavelet Denoising and Improved EEMD

2014 ◽  
Vol 556-562 ◽  
pp. 2677-2680 ◽  
Author(s):  
Ling Jie Meng ◽  
Jia Wei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Selection Criterion ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Wavelet Denoising ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Second Generation Wavelet

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

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