scholarly journals A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1406 ◽  
Author(s):  
Wentao Mao ◽  
Liyun Wang ◽  
Naiqin Feng
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Structural Feature ◽  
Group Structure ◽  
Experimental Results ◽  
Selection Strategy ◽  
Multi Objective ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Model

By using signal processing and statistical analysis methods simultaneously, many heterogeneous features can be produced to describe the bearings fault with more comprehensive and discriminant information. At same time, there may exist redundant or irrelevant information which will instead reduce the diagnosis performance. To solve this problem, it is necessary to conduct feature selection which tries to choose the most typical and discriminant features by evaluating their effect on fault status. However, if the structural relationship between features has not been considered well, some similar or redundant features are still probably chosen, which would introduce bias into the final diagnosis model. In this paper, a new fault diagnosis method of bearings based on structural feature selection is proposed to solve the aforementioned problem. Obeying the hypothesis that the features with strong relatedness have close coefficient distance, the proposed method aims to improve diagnosis performance via determining group structure in fault features. First, a new feature selection strategy is proposed by introducing a group identification matrix. Using this matrix, two evaluation criteria about intra-group feature correlation and inter-group feature difference are constructed by means of coefficient’s distance. Consequently, we get a multi-objective 0–1 integer programming problem by minimizing intra-group distance and maximizing inter-group distance simultaneously. Second, we use the multi-objective particle swarm optimization algorithm to solve this problem, and then determine the optimal group structure of features adaptively. Finally, a diagnosis model can be trained by support vector machine on the typical features extracted from these groups. Experimental results on four UCI datasets show the effectiveness of the proposed group feature selection strategy. Moreover, the experimental results on two bearing datasets (i.e., CWRU and IMS datasets) demonstrate that the proposed method can identify the inherent group structure in fault features, and then has better diagnosis performance compared with several state-of-the-art methods.

scholarly journals Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jingli Yang ◽  
Tianyu Gao ◽  
Shouda Jiang ◽  
Shijie Li ◽  
Qing Tang
Keyword(s):  
Fault Diagnosis ◽  
Noise Immunity ◽  
Rotating Machinery ◽  
Experimental Results ◽  
Vibration Signals ◽  
One Dimensional ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
Short Time

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.

scholarly journals A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

2021 ◽  
Vol 11 (23) ◽  
pp. 11116
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Chunting Liu
Keyword(s):  
Fault Diagnosis ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Driven ◽  
Total Cost ◽  
Operation And Maintenance ◽  
Diagnosis Method ◽  
Overall Performance ◽  
Diagnosis Model ◽  
The Cost

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

Fault Diagnosis Method Research of Avionics Systems Based on Testing

2014 ◽  
Vol 519-520 ◽  
pp. 1149-1154
Author(s):  
Wen Jun Zhao
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Diagnostic Testing ◽  
Automatic Test Equipment ◽  
Testing Strategy ◽  
Automatic Test ◽  
Location Accuracy ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
The Relationship

As for this problem that the equipment/devices maintenance and troubleshooting of new avionics systems is very difficult, the fault Diagnosis Method based on testing is proposed. This method is used to build fault diagnosis model and generate diagnostic testing strategy by establishing the relationship between the fault and test, and then the automatic test equipment is used to test for fault under the reasoning of the diagnosis inference, finally, fault conclusions are drawn. Application shows that this method is feasible, fault location accuracy is high and application prospect is broad.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

scholarly journals Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668529 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Bat Algorithm ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Machine Method ◽  
Training Samples ◽  
Kernel Parameter

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

2021 ◽  
Author(s):  
Hao DeChen ◽  
HuaLing Li ◽  
JinYing Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Network Structure ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Data Set ◽  
Public Data ◽  
Diagnosis Method ◽  
Diagnosis Model

Abstract Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

scholarly journals Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 359 ◽  
Author(s):  
Jianghua Ge ◽  
Guibin Yin ◽  
Yaping Wang ◽  
Di Xu ◽  
Fen Wei
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Support Vector ◽  
Distance Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Evaluation ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved.

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