scholarly journals Fault diagnosis based on dial-test data in datacenter networks

2019 ◽  
Vol 30 (5) ◽  
pp. 1035
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Datacenter Networks ◽  
Dial Test

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 of Pipeline Fault Diagnosis for Liquid Rocket Propulsion System

2012 ◽  
Vol 232 ◽  
pp. 305-309
Author(s):  
Yan Jun Li ◽  
Xiao Hui Peng ◽  
Yu Qiang Cheng ◽  
Jian Jun Wu
Keyword(s):  
Fault Diagnosis ◽  
False Alarm ◽  
Test Data ◽  
Simulation Analysis ◽  
Fault Simulation ◽  
Fault Isolation ◽  
Necessary Condition ◽  
Rocket Propulsion ◽  
Diagnosis Algorithm ◽  
Liquid Rocket

The applications of healthy monitoring and fault diagnosis's system can enhance the reliability and safety of the whole vehicle, which has significance to detect and isolate fault as early as possible and that tragedy can be avoided. In this paper, pipeline fault simulation analysis and fault diagnosis for LRE is studied as direct-inverse problem. Firstly, the failure model was constituted for pipeline to analysis the necessary condition for fault isolation. Then the conclusion that strategy of fault diagnosis was build by analysis the simulation result based on AMESim. Finally, the fault diagnosis algorithm was validated by test data. The results indicate that the fault diagnosis algorithm can detect fault exactly and effectively. There are no false alarm to normal test data and no false alarm to other components fault. Consequently the fault isolation result could be reached.

scholarly journals Fault Diagnosis of Traction Transformer Based on Bayesian Network

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4966 ◽  
Author(s):  
Yong Xiao ◽  
Weiguo Pan ◽  
Xiaomin Guo ◽  
Sheng Bi ◽  
Ding Feng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Test Data ◽  
Oil And Gas ◽  
Engineering Application ◽  
Field Tests ◽  
Fault Classification ◽  
Ratio Method ◽  
Single Experiment ◽  
Traction Power Supply

As the core equipment of a traction power supply system, the traction transformer is very important to ensure the safe and reliable operation of the system. At present, the three-ratio method is mainly used to distinguish transformer faults, whereas such a method has some defects, such as insufficient coding and over-general fault classification. At the same time, on-site maintenance personnel make an empirical judgment based on various test data, which is subjective and uncertain to a certain extent. For cases with multiple abnormal data and relatively complex conditions, on-site personnel often need to discuss and even dismantle the transformer to identify the fault, which is time-consuming and costly. In order to improve the effect of fault diagnosis for traction transformer, this paper uses Bayesian network to correlate the cause and effect of various tests and faults. By combining the results of field tests, the fault is diagnosed by the causal probability of the Bayesian network, rather than relying on the exception that occurred in a single experiment to judge its fault. The diagnosis results are more accurate and objective by using the Bayesian network. In this paper, the frequent test anomalies of the traction transformer are taken into account in the network, so that the network can more comprehensively analyze the operation situation of the traction transformer and judge the type of fault. According to field situations, based on the existing set of symptoms of the Bayesian network fault diagnosis, this paper further considers the insulation resistance, dielectric loss tangent value, oil and gas, power frequency voltage, and leakage current. By combining the association rules algorithm and the experience of the field operators, the cause–effect relationship of test data and the conditional probability parameters of the network are obtained. Then, the Bayesian network is constructed and used for traction transformer fault diagnosis. The case study shows that the four types of fault diagnosed using the Bayesian network model proposed in this paper are consistent with the fault types inspected by on-site operators, which shows promising engineering application prospects.

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.

Equipment Fault Diagnosis Based on Incomplete Rough Sets Theory

2012 ◽  
Vol 490-495 ◽  
pp. 1226-1230
Author(s):  
Yan Qin Su ◽  
Shan Gao ◽  
Ting Xue Xu
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Rough Sets ◽  
Rough Sets Theory ◽  
Model Based ◽  
Diagnosis Model ◽  
Sets Theory ◽  
Incorrect Data

There are redundant, incomplete and incorrect data in the equipment test data gained by the test equipments. The complete algorithms and attribution reduction algorithms were analyzed and the equipment fault diagnosis model based on Rough Sets Theory was given. Then, some equipment was diagnosed, and the results indicate that the diagnosis is effective and efficient.

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.

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