Control Change Cause Analysis-Based Fault Diagnostic Approach

2017 ◽  
Vol 12 (6) ◽  
pp. 1182-1191 ◽  
Author(s):  
Gang-Gang Wu ◽  
◽  
Zong-Xiao Yang ◽  
Gen-Sheng Li ◽  
Lei Song

How to identify the fault causes quickly and improve the efficiency of maintenance, which can reduce the fault disaster, has always been one of the key problems in equipments fault diagnosis. In this paper, a new qualitative fault diagnostic approach based on control change cause analysis (3CA) is proposed to identify the fault causes and fault risk index, which can be utilized to control the risk of equipment fault. We employed an existing method that was events and conditional factors analysis (ECFA+) to identify the analysis objects of 3CA, and put forward integrated methods including first principle-best practices approach, barrier failure analysis and prioritization rating code (PRC) matrix to accomplish control analysis, change analysis and significance rating of 3CA respectively, and those technical methods could be used to build the procedure diagram of identifying the content in each column of 3CA worksheet. According to the procedure of 3CA, we built a worksheet of 3CA for a vehicle engine fault, then fault causes and significance rating on behalf of the rating of fault risk index were determined. Meanwhile fault risk index had also been used to rank the fault causes, accomplishing fault diagnosis and verifying the availability or this method for fault diagnosis. The proposed approach can be able to identify fault causes of different fault modes that they have different risk index, and provide the fault causes rating that is the foundations of troubleshooting, which can mitigate and control fault disaster.

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Keyan Liu ◽  
Weijie Dong ◽  
Huanyu Dong ◽  
Jia Wei ◽  
Shiwu Xiao

After renewable energy distributed generator (DG) is connected to the power grid, traditional diverse-electric-information-based fault diagnosis approaches are not suitable for an active distributed network (ADN) due to the weak characteristics of fault current. Thus, this paper proposes a comprehensive nonformula fault diagnostic approach of ADN using only voltage as input. In the preprocess, sequential forward selection (SFS) and sequential backward selection (SBS) are utilized to optimize the input feature matrix of the sample in order to reduce the information redundancy of multiple measuring points in ADN. Then, a single “1-a-1” support vector machine (SVM) classifier is used for fault identification, and a multi-SVM, with radial basis function (RBF) as the kernel function, is applied to identify the location and fault type. To prove the proposed method is adaptable for ADN, two direct drive fans are used as a DG to test the IEEE 33 node model at every 10% of the line under three operating conditions that include all cases of distributed power generation in ADN. Results comparing real-time and historical data show that the proposed multi-SVM model reaches an average fault type diagnosis accuracy of 97.27%, with a fault identification accuracy of 96%. A backpropagation neural network is then compared to the proposed model. The results show the superior performance of the SBS-SFS optimized multi-SVM. This model can be usefully applied to the fault diagnosis of new energy sources with distributed power access to distribution networks.


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