scholarly journals A System Fault Diagnosis Method with a Reclustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhe Yang ◽  
Shi Ying ◽  
Bingming Wang ◽  
Yiyao Li ◽  
Bo Dong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Prior Knowledge ◽  
Language Model ◽  
Experimental Results ◽  
Log Analysis ◽  
Log Data ◽  
Data Support ◽  
Diagnosis Method ◽  
Low Dimensional

The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis.

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.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

2014 ◽  
Vol 536-537 ◽  
pp. 49-52
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Fault Classification ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis ◽  
Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

scholarly journals A bearing fault diagnosis method based on the low-dimensional compressed vibration signal

2015 ◽  
Vol 7 (7) ◽  
pp. 168781401559344 ◽  
Author(s):  
Xinpeng Zhang ◽  
Niaoqing Hu ◽  
Lei Hu ◽  
Ling Chen ◽  
Zhe Cheng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Low Dimensional

scholarly journals A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaowei Wang ◽  
Zhenhong Jiang ◽  
Lingxi Peng
Keyword(s):  
Deep Learning ◽  
Dimensional Space ◽  
Language Model ◽  
Experimental Results ◽  
Matching Model ◽  
Job Matching ◽  
Textual Information ◽  
Data Matching ◽  
Subject Term ◽  
Low Dimensional

In this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person-post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person-post matching model are compared with a variety of person-post matching methods in the actual recruitment dataset, and the experimental results are analyzed.

scholarly journals A CNN-ABiGRU method for Gearbox Fault Diagnosis

2022 ◽  
Vol 16 ◽  
pp. 440-446
Author(s):  
Xiaoyang Zheng ◽  
Zeyu Ye ◽  
Jinliang Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Prior Knowledge ◽  
Time Sequence ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Generalization Performance ◽  
Proposed Model ◽  
Gated Recurrent Unit

As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy.

scholarly journals Application of DBN and GWO-SVM in analog circuit fault diagnosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiyuan Su ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Experimental Results ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Highpass Filter ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Factor C

AbstractFor large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

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.

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