A New Framework of Simultaneous-Fault Diagnosis Using Pairwise Probabilistic Multi-Label Classification for Time-Dependent Patterns

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.