Fault Reasoning and Diagnosis Technology Based on ACO & CBR

Abstract:

Due to the inaccuracy of reasoning conclusion because of the discrepancy among the cases ontology in the process of case reuse or revision, a new reasoning method for fault diagnosis based on ACO and CBR is proposed by this paper. This method uses CBR to reason new cases firstly, if it can match successfully brings to the corresponding results, else use ACO to reason the cases which have not matching in the case-library. As a result, the accuracy and efficiency of fault diagnosis are improved greatly and use the characteristic of the strong memory in CBR to repair the shortcoming of ACO reasoning method that can improve it capability. The method adopting fault cause-symptom matrix to describe the cases and case-library has many good characteristics of conciseness, convenience and extendibility.

• Periodical: Advanced Materials Research (Volumes 655-657)
• Pages: 1722-1729
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.655-657.1722
• Online since: January 2013
• Authors: Yang Zhao, Yun Xiong, Xin Qing Wang, Hong Wei Li, Hui Jie Zhu
• Keywords: Ant Colony Optimization (ACO), Case-Based Reasoning (CBR), Fault Cause-Symptom Matrix, Fault Diagnosis

References

[1] An S H, Kim G H, Kang K I. A case-based reasoning cost estimating model using experience by analytic hierarchy process [J]. Building and Environment. Vol. 42-7 (2007), pp.2573-2579.

DOI: 10.1016/j.buildenv.2006.06.007

[2] Wang Shengfa, Gu Xinjian, Guo Jianfeng, et al. Research and application of ontology modeling for product design based on case reasoning [J]. Chinese Journal of Mechanical Engineering. Vol. 43-3 (2007), pp.112-117. (In Chinese).

DOI: 10.3901/jme.2007.03.112

[3] Han xue, Feng yuxiang. Negotiation support system based on case-based reasoning [J]. Control and Decision. Vol. 23-7(2008), pp.792-794. (In Chinese).

[4] Feng wenjiang, Liu zhen, Qin chunling. Application of Case-Based Reasoning in Cognitive Engine [J]. Pattern Recognition and Artificial Intelligence. Vol. 24-3(2011), pp.400-404. (In Chinese).

[5] Mariano C E, Morales E F. Distributed reinforcement learning for multiple objective optimization problems[C]. Evolutionary Computation 2000, Proc of 2000 Congress. California. Vol. 1(2000), pp.188-195.

DOI: 10.1109/cec.2000.870294

[6] Wang Lei, Wu Qi-di. An application of ant system algorithm in continuous space optimization [J]. Control and Decision. Vol. 18-1(2003), pp.45-48.

[7] Gao lixin, Zhang jianyu, Xu yonggang, et al. Fault Diagnosis Technology Based on CBR＆RBR for Gearbox of the Rolling Mills [J]. Journal of Beijing University of technology. Vol. 36-9(2010), pp.1174-1180. (In Chinese).

[8] Kim K O, Zuo M, et al. Two fault classification methods for large systems when available data are limited [J]. Reliability Engineering and System Safety. Vol. 92(2007), pp.585-592.

DOI: 10.1016/j.ress.2006.02.001

[9] Wong F S Y, Chuah K B, Venuvinod P K. Automated in-spection process planning: algorithmic inspection feature recognition and inspection case representation for CBR [J]. Robotics and Computer-Integrated Manufacturing. Vol. 22-1(2006), pp.56-68.

DOI: 10.1016/j.rcim.2005.02.005

[10] Babnik T, Cubina R. Two approaches to power transformer fault classification based on protection signals [J]. Int J. Electr Power Energy Syst. Vol. 24(2002), pp.459-468.

DOI: 10.1016/s0142-0615(01)00058-8