Simultaneous faults diagnosis for automotive ignition patterns.
DOI: 10.1109/ICMLC.2011.6016890 Conference: International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, China, July 10-13, 2011, Proceedings
Many practical applications have the property that different possible faults may appear at one time. Simultaneous faults diagnosis is referred to accurately diagnose the possible faults based on the symptoms from an observed pattern. There are two key challenges in this kind of problem: 1) the symptoms of different faults are mixed or combined into one (input) pattern which makes accurate diagnosis difficult, 2) the preparation of a large amount of training patterns because there are many different combinations of faults. We proposed a framework to effectively resolve these challenges using feature extraction and multi-label probabilistic classification. This framework has been applied and verified in the domain of automotive ignition patterns.
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ABSTRACT: Simultaneous-fault diagnosis is a common problem in many applications and well-studied for time-independent patterns. However, most practical applications are of the type of time-dependent patterns. In our study of simultaneous-fault diagnosis for time-dependent patterns, two key issues are identified: 1) the features of the multiple single faults are mixed or combined into one pattern which makes accurate diagnosis difficult, 2) the acquisition of a large sample data set of simultaneous faults is costly because of high number of combinations of single faults, resulting in many possible classes of simultaneous-fault training patterns. Under the assumption that the time-frequency features of a simultaneous fault are similar to that of its constituent single faults, these issues can be effectively resolved using our proposed framework combining feature extraction, pairwise probabilistic multi-label classification, and decision threshold optimization. This framework has been applied and verified in automotive engine-ignition system diagnosis based on time-dependent ignition patterns as a test case. Experimental results show that the proposed framework can successfully resolve the issues.IEEE Transactions on Industrial Electronics 08/2013; 60(8):3372-3385. DOI:10.1109/TIE.2012.2202358 · 6.50 Impact Factor
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