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Sensor technology developments provide a basis for effective fault diagnosisin manufacturing systems. However, the limited number of sensors due tophysical constraints or undue costs hinders the accurate diagnosis in theactual process. In addition, time-varying operational conditions that generatenonstationary process faults and the correlation information in the processrequire to consider for accurate fault diagnosis in the manufacturing systems.This article proposes a novel fault diagnosis method: clustering spatiallycorrelated sparse Bayesian learning (CSSBL), and explicitly demonstrates itsapplicability in a multistation assembly system that is vulnerable to the abovechallenges. Specifically, the method is based on a practical assumption that itwill likely have a few process faults (sparse). In addition, the hierarchicalstructure of CSSBL has several parameterized prior distributions to address theabove challenges. As posterior distributions of process faults do not haveclosed form, this paper derives approximate posterior distributions throughVariational Bayes inference. The proposed method's efficacy is provided throughnumerical and real-world case studies utilizing an actual autobody assemblysystem. The generalizability of the proposed method allows the technique to beapplied in fault diagnosis in other domains, including communication andhealthcare systems.

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems