A nonnegative garrote-based method for multivariate fault isolation

Fault isolation is an essential step in multivariate statistical process monitoring, which discovers the cause of the detected fault. The common adopted techniques for fault isolation, such as contribution plots, reconstruction analysis, branch and bound, etc., often suffer from the problems of the smearing effect, insufficient historical fault data, and heavy computational burden. In this paper, the nonnegative garrote (NNG) estimator is utilized for multivariate statistical fault isolation, which solves the problems of the existing methods. The proposed method is based on the finding that there is equivalence between fault isolation and variable selection in discriminant analysis. Different from the existing methods, the NNG-based fault isolation method does not suggest a single set of faulty variables. Instead, it provides a sequence of process variables according to their responsibilities for the detected fault. Such results are informative for diagnosing the root cause of the fault.