A method of fault analysis for test generation and fault diagnosis

1988 ◽  
Vol 7 (7) ◽  
pp. 813-833 ◽  
Author(s):  
H. Cox ◽  
J. Rajski
Keyword(s):  
Fault Diagnosis ◽  
Test Generation ◽  
Fault Analysis

The Wavelet Analysis and Processing of Weak Vibration Signal

2011 ◽  
Vol 189-193 ◽  
pp. 1426-1431
Author(s):  
Ze Ning Xu ◽  
Hong Yu Liu ◽  
Yong Guo Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Basic Feature ◽  
Wavelet Function ◽  
Fault Analysis ◽  
Bearing Fault Diagnosis ◽  
Gear Fault ◽  
Vibratory Signals

Signal measuring is an important link in machine fault diagnosis. Accurate and reliable fault signals can be achieved by reasonable signal measuring. When the distance between sensor and measuring gear or bearing is comparatively far, the collected signals became weak and disturbed by other vibratory signals in equipments on bearing and gear fault analysis. Useful signals often were submerged in powerful noise, so caused difficult in extracting fault feature. In this paper, according to the feature of vibratory signals in machine test, wavelet analysis basic theory was applied on researching basic feature of wavelet analysis. By selecting suitable wavelet function and applying wavelet elimination noise technology the signal to noise ratio of signal was raised, thus the vibratory impact component can be measured in weak signals. Finally, wavelet analysis was applied on bearing fault diagnosis.

Fault Diagnosis of the Foundation Brake Rigging System Based on Fault Tree and Bayesian Network

2016 ◽  
Vol 693 ◽  
pp. 1734-1740 ◽  
Author(s):  
Dan Wang ◽  
Ying Tian ◽  
Tai Yong Wang ◽  
Shi Feng Ye ◽  
Qiong Liu
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Bayesian Network ◽  
Fault Tree ◽  
Fault Analysis ◽  
Inference Process ◽  
Tree Algorithm ◽  
Junction Tree ◽  
Bayesian Tree

Based on the analysis of the advantages and limits of the traditional fault tree and Bayesian network in fault diagnosis, the method that building the fault Bayesian network based on fault tree is proposed in this paper. The paper introduces the correspondences between elements of the fault tree and the fault Bayesian network, also describes the inference process of the junction tree algorithm in the fault Bayesian network. Then with the foundation brake rigging system of CRH380AL EMU as an example, we build up the fault tree, complete its transmission to the fault Bayesian network, proving the superiority of the fault Bayesian tree in fault analysis of the complex system at last.

scholarly journals Fault Analysis and Condition Monitoring of Transformer

2020 ◽  
Vol 9 (4) ◽  
pp. 1491-1494
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Stability Condition ◽  
Power Transformer ◽  
Fault Analysis ◽  
System Stability ◽  
Load Management ◽  
Matlab Simulink ◽  
Monitoring Techniques ◽  
Transformer Model

The unscheduled outages of transformers, due to unexpected failures are creating more problems for load management and system stability. Condition monitoring is done on power transformer those monitor various parameters predict fault accruing possibility and reduced unscheduled outage. In this paper, different condition monitoring techniques have discussed. The transformer model is prepared with healthy and faulty conditions using MATLAB Simulink. L-G, L-L-G, and L-L-L-G fault have been discussed in this paper. Various parameters are used for condition monitoring and fault diagnosis.

scholarly journals A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhu Huang ◽  
Tao Wang ◽  
Wei Liu ◽  
Luis Valencia-Cabrera ◽  
Mario J. Pérez-Jiménez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Analysis ◽  
Fault Prediction ◽  
P Systems ◽  
Analysis Method ◽  
Spiking Neural P Systems ◽  
Three Phase ◽  
Fuzzy Production Rules ◽  
Reasoning Algorithm

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

Condition Monitoring of PMSM and Stator-inter Turn Fault Diagnosis by Advanced Signal Processing Technique

2021 ◽  
Vol 23 (07) ◽  
pp. 1419-1430
Author(s):  
Khadim Moin Siddiqui ◽  
◽  
Farhad Ilahi Bakhsh ◽  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Dynamic Performance ◽  
Processing Technique ◽  
Industrial Applications ◽  
Fault Analysis ◽  
Industrial Plant ◽  
Signal Processing Technique ◽  
Permanent Magnet Synchronous Motors

In the present time, Permanent Magnet Synchronous Motors (PMSMs) are extensively used in many industrial applications due to its advantages over conventional synchronous motor. The PMSM is compact and efficient with high dynamic performance, thus having more advantages such as light weight, small size and bulky burden ability. When PMSMs are failed during the operation then large revenue losses occurs for industries. Hence, it is essential to diagnose these faults before occurring, for protection of any industrial plant. In the paper, firstly a comprehensive review of condition monitoring has been done for PMSM faults and their diagnostics techniques. From review, it is found that the stator inter-turn fault diagnosis has been the challenging task for many researchers. Hence, the work has been extended for fault analysis of stator inter-turn under transient conditions, which is effectively analyzed with the help of advanced signal processing technique.

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