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.

传感器技术发展、故障诊断、制造系统、空间相关稀疏贝叶斯学习和多站装配系统是该研究的五个关键词。该研究提出了一种新颖的故障诊断方法 —— 聚类空间相关稀疏贝叶斯学习，并通过数值和实际案例研究在一个易受以上挑战影响的多站装配系统中展示了其适用性。该方法的推广性使得它可以应用于包括通信和医疗系统在内的其他领域的故障诊断。