Recursive Spectral Meta-Learner for Online Combining Different Fault Classifiers.

This paper considers the problem of fault classification when different fault classifiers are performed simultaneously. Based on spectral meta-learner (SML) proposed by Parisi et al., its recursive version, i.e., recursive SML (RSML) is developed for online combining the potentially conflicting classification information. Considering different statistical properties of faults occurring at different time intervals, the binary classification information is recursively utilized. By introducing a forgetting factor, the leading eigenvector of the estimate of the time-varying covariance matrix is used as the weight vector for each classifier. Rank-one modification is then used for calculating the eigenvector in order to reduce the online computational complexity. The performance of RSML is strictly analyzed in a statistical sense, including the effect of recursive calculation and conditional dependence of different classifiers on the weight vector. Compared with majority voting and SML, a higher balanced accuracy of RSML can be verified by the benchmark Tennessee Eastman process. [ABSTRACT FROM PUBLISHER]