Data Mining Approaches to Software Fault Diagnosis

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

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Case studies of fault diagnosis and energy saving in buildings using data mining techniques

2016 IEEE International Conference on Automation Science and Engineering (CASE) ◽

10.1109/coase.2016.7743463 ◽

2016 ◽

Author(s):

Zhijin Cheng ◽

Qianchuan Zhao ◽

Fulin Wang ◽

Zheliang Chen ◽

Yi Jiang ◽

...

Keyword(s):

Data Mining ◽

Fault Diagnosis ◽

Energy Saving ◽

Case Studies ◽

Data Mining Techniques ◽

Using Data

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Fault Diagnosis of Rotating Machinery Using Data Mining and CLIPS-Based Expert System

2008 ISECS International Colloquium on Computing, Communication, Control, and Management ◽

10.1109/cccm.2008.120 ◽

2008 ◽

Cited By ~ 1

Author(s):

Dongyang Dou ◽

Yingkai Zhao

Keyword(s):

Data Mining ◽

Fault Diagnosis ◽

Expert System ◽

Rotating Machinery ◽

Using Data

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The Software Support Analysis of Information System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2965 ◽

2014 ◽

Vol 644-650 ◽

pp. 2965-2968

Author(s):

Hua Wang ◽

Xian Yu Li ◽

Xue Ning Wang ◽

Wei Na Liu

Keyword(s):

Information System ◽

Fault Diagnosis ◽

Integrated Model ◽

Fast Recovery ◽

Software Support ◽

Main Factors ◽

Software Fault ◽

System Effectiveness

Based upon the synopsis of software support of information system, this paper puts forward main factors, integrated model and phase tasks of software support of information system, and gives description to software supportability analysis technology, software fault diagnosis technology, software fast recovery technology and software remote support technology so as to increase software support capabilities and information system effectiveness.

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Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

IEEE Transactions on Power Electronics ◽

10.1109/tpel.2021.3131293 ◽

2021 ◽

pp. 1-1

Author(s):

Arash Moradzadeh ◽

Behnam Mohammadi-ivatloo ◽

Kazem Pourhossein ◽

Amjad Anvari-Moghaddam

Keyword(s):

Systematic Review ◽

Data Mining ◽

Fault Diagnosis ◽

Electronic Systems ◽

Power Electronic

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A Local Approach and Comparison with Other Data Mining Approaches in Software Application

Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts - Advances in Information Quality and Management ◽

10.4018/978-1-5225-1884-6.ch001 ◽

2017 ◽

pp. 1-26

Author(s):

QingE Wu ◽

Weidong Yang

Keyword(s):

Data Mining ◽

Fault Diagnosis ◽

Forecast Accuracy ◽

Software System ◽

Local Approach ◽

Test Analysis ◽

Software Application ◽

Software Aging ◽

Optimum Approach ◽

Sensitivity Specificity

In order to complete an online, real-time and effective aging detection to software, this paper studies a local approach that is also called a fuzzy incomplete and a statistical data mining approaches, and gives their algorithm implementation in the software system fault diagnosis. The application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis is discussed. The performance of each approach is evaluated from the sensitivity, specificity, accuracy rate, error classified rate, missed classified rate, and run-time. An optimum approach is chosen from several approaches to do comparative study. On the data of 1020 samples, the operating results show that the fuzzy incomplete approach has the highest sensitivity, the forecast accuracy that are 96.13% and 94.71%, respectively, which is higher than those of other approaches. It has also the relatively less error classified rate is or so 4.12%, the least missed classified rate is or so 1.18%, and the least runtime is 0.35s, which all are less than those of the other approaches. After the performance, indices are all evaluated and synthesized, the results indicate the performance of the fuzzy incomplete approach is best. Moreover, from the test analysis known, the fuzzy incomplete approach has also some advantages, such as it has the faster detection speed, the lower storage capacity, and does not need any prior information in addition to data processing. These results indicate that the mining approach is more effective and feasible than the old data mining approaches in software aging detection.

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