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.

翻译：摘要：传感器技术的发展为制造系统中的有效故障诊断提供了基础。然而，由于物理限制或成本过高导致的传感器数量有限，阻碍了实际过程中的准确诊断。此外，时变运行条件会产生非平稳过程故障，且过程中的相关信息也需要考虑，以实现制造系统中的精确故障诊断。本文提出了一种新的故障诊断方法：聚类空间相关稀疏贝叶斯学习（CSSBL），并明确展示了其在易受上述挑战影响的多工位装配系统中的适用性。具体而言，该方法基于一个实际假设：过程故障数量较少（稀疏性）。此外，CSSBL的层次结构包含多个参数化的先验分布以应对上述挑战。由于过程故障的后验分布没有闭式解，本文通过变分贝叶斯推理推导出近似后验分布。通过数值仿真和基于实际车身装配系统的案例研究，验证了所提方法的有效性。该方法的泛化性使其可应用于其他领域的故障诊断，包括通信和医疗系统。