scholarly journals A Neutrosophic Set Based Fault Diagnosis Method Based on Multi-Stage Fault Template Data

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 346 ◽  
Author(s):  
Wen Jiang ◽  
Yu Zhong ◽  
Xinyang Deng
Keyword(s):  
Fault Diagnosis ◽  
Weighted Averaging ◽  
Neutrosophic Set ◽  
Uncertain Information ◽  
Traditional Methods ◽  
Multiple Features ◽  
Fault Type ◽  
Multi Stage ◽  
Actual Environment ◽  
Diagnosis Method

Fault diagnosis is an important issue in various fields and aims to detect and identify the faults of systems, products, and processes. The cause of a fault is complicated due to the uncertainty of the actual environment. Nevertheless, it is difficult to consider uncertain factors adequately with many traditional methods. In addition, the same fault may show multiple features and the same feature might be caused by different faults. In this paper, a neutrosophic set based fault diagnosis method based on multi-stage fault template data is proposed to solve this problem. For an unknown fault sample whose fault type is unknown and needs to be diagnosed, the neutrosophic set based on multi-stage fault template data is generated, and then the generated neutrosophic set is fused via the simplified neutrosophic weighted averaging (SNWA) operator. Afterwards, the fault diagnosis results can be determined by the application of defuzzification method for a defuzzying neutrosophic set. Most kinds of uncertain problems in the process of fault diagnosis, including uncertain information and inconsistent information, could be handled well with the integration of multi-stage fault template data and the neutrosophic set. Finally, the practicality and effectiveness of the proposed method are demonstrated via an illustrative example.

scholarly journals Fault diagnosis based on TOPSIS method with Manhattan distance

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983327 ◽  
Author(s):  
Wen Jiang ◽  
Meijuan Wang ◽  
Xinyang Deng ◽  
Linfeng Gou
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Weighted Averaging ◽  
Manhattan Distance ◽  
Uncertain Information ◽  
Ideal Solution ◽  
Solution Approach ◽  
Intuitionistic Fuzzy ◽  
Fault Type

Fault diagnosis is important for the maintenance of machinery equipment. Due to the randomness and fuzziness of fault, the relationship between fault types and their characteristics are complicated. Therefore, the determination of fault type is a challenging part of machinery fault diagnosis with the traditional method. To tackle this problem, a fault diagnosis approach based on the technique for order performance by similarity to ideal solution with Manhattan distance is presented in this article. First, the similarity measure between the fault model and the detection sample is constructed based on the Manhattan distance. Then, the similarity is transformed into intuitionistic fuzzy set and the generated intuitionistic fuzzy set is fused by the intuitionistic fuzzy weighted averaging operator. On this basis, the technique for order performance by similarity to the ideal solution approach is utilized to obtain the final rank to ascertain the fault type. The proposed method can handle an intricate relationship between multiple fault types and their various fault characteristics and better express uncertain information. Finally, a fault diagnosis example of the machine rotor and comparative study are conducted to illustrate the application and the effectiveness of the proposed method.

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.

scholarly journals A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6223
Author(s):  
Lei Jin ◽  
Qing Chen ◽  
Jinjie Ji ◽  
Xiaotong Zhou
Keyword(s):  
Fault Diagnosis ◽  
Power Grid ◽  
Circuit Breaker ◽  
Information Loss ◽  
Uncertain Information ◽  
Skyline Query ◽  
Data Set ◽  
Information Set ◽  
Diagnosis Method ◽  
Query Algorithm

After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

scholarly journals An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1041 ◽  
Author(s):  
Yang Liu ◽  
Lixiang Duan ◽  
Zhuang Yuan ◽  
Ning Wang ◽  
Jianping Zhao
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Traditional Methods ◽  
Learning Abilities ◽  
Fault Features ◽  
Correlation Criterion ◽  
Long Time ◽  
Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

scholarly journals Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Mingyue Tan ◽  
Jiming Li ◽  
Xiangqian Chen ◽  
Xuezhen Cheng
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Petri Nets ◽  
Power Grid ◽  
New Method ◽  
Uncertain Information ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Petri Nets ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

Research on Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine

2019 ◽  
Vol 12 (5) ◽  
pp. 408-413
Author(s):  
Yuancheng Li ◽  
Xiaohan Wang ◽  
Yingying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Dissolved Gas ◽  
Traditional Methods ◽  
Electric System ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Transformer Fault

Background: Transformer is one of the most important pivot equipment in an electric system which undertakes major responsibility. Therefore, it is very important to identify the fault of the transformer accurately and transformer fault diagnosis technology becomes one topic with great research value. Methods: In this paper, after analyzing the shortcomings of traditional methods, we have proposed a transformer fault diagnosis method based on Online Sequential Extreme Learning Machine (OS-ELM) and dissolved gas-in-oil analysis. This method has better precision than some commonly used methods at present. Furthermore, OS-ELM is more efficient than ELM. In addition, we analyze the effect of different parameter selection on the performance of the model by contrast experiments. Results: The experimental result shows that OS-ELM has certain promotion in precision than some traditional methods and can obviously improve the speed of training than ELM. Besides, it is known that the number of neurons in the hidden layer and the size of dataset have a great effect on the model. Conclusion: The transformer fault diagnosis method based on OS-ELM can effectively identify the faults and more efficient than ELM.

scholarly journals Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Naiquang Su ◽  
Xiao Li ◽  
Qinghua Zhang ◽  
Zhiqiang Huo ◽  
Xavier Chiementin
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Large Scale ◽  
Evidence Theory ◽  
Rotating Machinery ◽  
Uncertain Information ◽  
Sample Distribution ◽  
Multiple Data ◽  
Diagnosis Method ◽  
Conflict Information

Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.

Transformer Winding Deformation Fault Diagnose Based on FRA and Energy Feature Extraction by Wavelet Packet

2012 ◽  
Vol 490-495 ◽  
pp. 1486-1490
Author(s):  
Su Xiang Qian ◽  
Qi Du ◽  
Xiao Jun Gu ◽  
Jia You Song
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Fault Diagnose ◽  
Characteristic Energy ◽  
Fault Type ◽  
Different Types ◽  
Transformer Winding ◽  
Diagnosis Method ◽  
Different Response ◽  
Transformer Fault

When different types and extent of faults occurs at transformer winding, the energy of the signals in different frequency bands will change. So it can calculate the characteristic energy of different response signals at different states to determine whether the winding failure. The transformer fault diagnosis method based on FRA and characteristic energy extraction is presented, the maximum cross-correlation between the signal and the wavelet was taken as criterion to choose the wavelet. The method is verified by test. Experimental results show that this method can diagnose winding fault type and extent effectively, and improve the sensitivity of fault diagnosis.

Analysis and Comparison of Locomotive Traction Motor Intelligent Fault Diagnosis Methods

2011 ◽  
Vol 97-98 ◽  
pp. 994-1002
Author(s):  
Zhan Peng Chen ◽  
Zhuo Wang ◽  
Li Min Jia ◽  
Guo Qiang Cai
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Procedures ◽  
Traction Motor ◽  
Intelligent Diagnosis ◽  
Existing Problems ◽  
Advantages And Disadvantages ◽  
Fault Type ◽  
Train Operation ◽  
Diagnosis Method ◽  
Traction Power

Train operation safety is the most important and the most basic requirement. Locomotive traction motor is the train operation of traction power equipment, whose reliability relates directly to the train operation safety. And locomotive traction motor fault diagnosis is to ensure the reliability of the traction motor scooter important technique means. Through the locomotive pulling motor failure diagnosis method's research, the traction motor typical fault type has been summarized, the main intelligent diagnosis method principle has been narrated, the main principles of the intelligent diagnosis, diagnostic procedures, and their advantages and disadvantages are described in detail, the existing problems in the field and future trends are pointed out finally.

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