scholarly journals The Method of Communication System Fault Diagnosis Based on Deep Belief Net

2021 ◽  
Vol 15 ◽  
pp. 978-985
Author(s):  
Juan Li ◽  
Bin Chen
Keyword(s):  
Fault Diagnosis ◽  
Communication System ◽  
Health Management ◽  
Bottom Layer ◽  
Original Data ◽  
Fault Classification ◽  
Deep Belief Net ◽  
Communication Experiment ◽  
And Training ◽  
Communication Equipment

To meet the need of fault diagnosis for military communication system, an effective method based on deep belief (DBN) net is proposed. During the fault diagnosis, the bottom layer of DBN model is used to receive the input fault signals to extract the fault features and the fault classification results will be outputted after softmax classified. Accordingly, algorithms for DBN model and training and RBM parameter learning have been designed. To reduce the running time, parallel solutions based on MapReduce framework have been provided. In order to test and verify the effect of DBN fault diagnosis, the communication experiment system is built in the laboratory which the output signals of the transmitter and the receiver are measured and collected as the original data for further learning and training. Compared with the traditional fault diagnosis methods, it can be found that DBN method has high accuracy in fault diagnosis and the process is simple and friendly. It is impossible to realize real-time diagnosis and online diagnosis for the communication system. The research can be applicated to the health management of communication equipment, and it will provide advanced technical support and software program for the health of communication equipment

scholarly journals Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 228 ◽  
Author(s):  
Ling Wang ◽  
Dongfang Zhou ◽  
Hui Tian ◽  
Hao Zhang ◽  
Wei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Health Management ◽  
Learning Algorithm ◽  
Training Model ◽  
Supervised Machine Learning ◽  
Fault Classification ◽  
Linear Discriminant ◽  
Diagnosis Rate ◽  
Parametric Fault

The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.

scholarly journals Research on typical fault classification method of DC-DC converter

2021 ◽  
Vol 39 (1) ◽  
pp. 71-76
Author(s):  
Zhao Xu ◽  
Yitong Zhang ◽  
Zhen Pan ◽  
Chengzhi Chi ◽  
Xiaobin Liu
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Original Data ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Core Component ◽  
Feature Extraction And Selection ◽  
Use Efficiency ◽  
Integrated Modular Avionics

DC-DC converter is the core component of power conversion module of integrated modular avionics. Condition monitoring and fault diagnosis of DC-DC converter can effectively improve the reliability of avionics equipment, reduce the maintenance cost and greatly improve the use efficiency of aircraft. In this paper, firstly, a typical DC-DC converter model based on SEPIC topology is designed in PSPICE environment, and the failure modes of DC-DC converter are analyzed. Secondly, the typical fault types of DC-DC converter are simulated, and the corresponding original data are obtained through simulation. Finally, the processing framework including data preprocessing, feature extraction and selection, and multi model fusion is used to do fault classification of the DC-DC converter. The fault diagnosis of the converter is simulated. Simulation results show the effectiveness of the proposed method.

scholarly journals Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm

2019 ◽  
Vol 16 (23) ◽  
pp. 4868 ◽  
Author(s):  
Zhongxin Chen ◽  
Feng Zhao ◽  
Jun Zhou ◽  
Panling Huang ◽  
Xutao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Sound Intensity ◽  
Original Data ◽  
National Standards ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Matlab Simulation ◽  
Independent Components

When a part of the loader’s gearbox fails, this can lead to equipment failure due to the complex internal structure and the interrelationship between the parts. Therefore, it is imperative to research an efficient strategy for transmission fault diagnosis. In this study, the non-contact characteristics of noise diagnosis using sound intensity probes were used to collect noise signals generated under gear breaking conditions. The independent component analysis (ICA) technique was applied for feature extraction from the original data and to reduce the correlation between the signals. The correlation coefficient between the independent components and the source data was used as the input parameters of the support vector machine (SVM) classifier. The separation of the independent components was achieved by MATLAB simulation. The misdiagnosis rate was 5% for 40 sets of test data. A 13-point test platform for noise testing of the loader gearbox was built according to Chinese national standards. Source signals under the normal and fault conditions were analyzed separately by ICA and SVM algorithms. In this case, the misdiagnosis rate was 7.5% for the 40 sets of experimental test data. This proved that the proposed method could effectively realize fault classification and recognition.

scholarly journals Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baokun Han ◽  
Sixiang Jia ◽  
Guifang Liu ◽  
Jinrui Wang
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Extraction Process ◽  
Fault Classification ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Excellent Performance ◽  
Adversarial Networks ◽  
And Training

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

scholarly journals Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4424
Author(s):  
Udeme Inyang ◽  
Ivan Petrunin ◽  
Ian Jennions
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Network Models ◽  
Health Condition ◽  
Limited Attention ◽  
Neural Network Models ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Multiple Fault Diagnosis ◽  
Non Gaussian

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

Fault Diagnosis of Rolling Bearings Based on LLE_KFDA

2009 ◽  
Vol 626-627 ◽  
pp. 529-534 ◽  
Author(s):  
Guang Bin Wang ◽  
Y.I. Liu ◽  
X.Q. Zhao
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rolling Bearing ◽  
Original Data ◽  
Locally Linear Embedding ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Nonlinear Dimension ◽  
Dimension Space

Locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimension reduction. In this paper,LLE manifold learning algorithm is introduced into the field of equipment fault diagnosis firstly, a method of the fault diagnosis based on LLE_KFDA is proposed. By LLE algorithm, original sample data is directly mapped to its’ intrinsical dimension space,which data still keep primary nonlinear form. then via kernel fisher discriminant analysis(KFDA), the characteristics data in intrinsical dimension space are mapped into knernel high-dimensional linear space,and then different fault data are discriminated based on a criterion of between-class and insid-class deviatione ratio maximum. LLE_KFDA algorithm is based on original data, avoided from fall of pattern recognition ability which caused by inappropriate or blind choice of the feature parameters in the traditional fault diagnosis method.The experiment to fault diagnosis of rolling bearing shows this method can effectively identify the equipment fault pattern, diagnostic result is good.

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