Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory

2012 ◽  
Vol 241-244 ◽  
pp. 288-292
Author(s):  
Zhi Song Wang ◽  
Li Wei Tang ◽  
Wen Wen Yu ◽  
Jin Hua Cao
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Evidence Theory ◽  
Basic Belief ◽  
Combination Rule ◽  
Fusion Model ◽  
Minkowski Distance ◽  
Model Based ◽  
Feature Parameters ◽  
Diagnosis Method

Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

A simulation model based fault diagnosis method for bearings

2018 ◽  
Vol 34 (6) ◽  
pp. 3857-3867 ◽  
Author(s):  
Wenlei Song ◽  
Jiawei Xiang ◽  
Yongteng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Simulation Model ◽  
Model Based ◽  
Diagnosis Method

A Model-Based Fault Detection Methodology for Vacuum Circuit Breaker

2013 ◽  
Vol 321-324 ◽  
pp. 739-742
Author(s):  
Yu Huang Zheng
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Dynamic Model ◽  
Test Data ◽  
High Speed ◽  
Circuit Breaker ◽  
Theoretical Data ◽  
Experimental Results ◽  
Model Based ◽  
Vacuum Circuit Breaker

Vacuum circuit breaker becomes more and more complicated, integrated, high-speed and intellectualized. To insure vacuum circuit breaker in its good conditions, the function of fault diagnosis gets more important than before in the process of repairing. This paper is addressed a model-based fault detection methodology for vacuum circuit breaker. At first, the dynamic model of vacuum circuit breaker is built. Secondly, DTW algorithm is introduced to compute the similarity value between the test data and the theoretical data. At last, the value comparison between the similarity and the threshold concludes whether a fault has occurred or the vacuum circuit breaker has potential hazardous effects. The experimental results show that this method is effective.

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.

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.

scholarly journals Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

2019 ◽  
Vol 14 (3) ◽  
pp. 329-343 ◽  
Author(s):  
Yukun Dong ◽  
Jiantao Zhang ◽  
Zhen Li ◽  
Yong Hu ◽  
Yong Deng
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Distance Function ◽  
Evidence Theory ◽  
Sensor Data ◽  
Combination Rule ◽  
Sensor Data Fusion ◽  
Real Application ◽  
Belief Entropy

Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

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