  Sensor technology developments provide a basis for effective fault diagnosis
in manufacturing systems. However, the limited number of sensors due to
physical constraints or undue costs hinders the accurate diagnosis in the
actual process. In addition, time-varying operational conditions that generate
nonstationary process faults and the correlation information in the process
require to consider for accurate fault diagnosis in the manufacturing systems.
This article proposes a novel fault diagnosis method: clustering spatially
correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its
applicability in a multistation assembly system that is vulnerable to the above
challenges. Specifically, the method is based on a practical assumption that it
will likely have a few process faults (sparse). In addition, the hierarchical
structure of CSSBL has several parameterized prior distributions to address the
above challenges. As posterior distributions of process faults do not have
closed form, this paper derives approximate posterior distributions through
Variational Bayes inference. The proposed method's efficacy is provided through
numerical and real-world case studies utilizing an actual autobody assembly
system. The generalizability of the proposed method allows the technique to be
applied in fault diagnosis in other domains, including communication and
healthcare systems.
