Fault Diagnosis Using an Enhanced Relevance Vector Machine (RVM) for Partially Diagnosable Multistation Assembly Processes
Fault Diagnosis Using an Enhanced Relevance Vector Machine (RVM) for Partially Diagnosable Multistation Assembly Processes
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DOI：
10.1109/tase.2012.2214383
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发表时间：
2013
影响因子：
5.6
通讯作者：
K. Bastani;Z. Kong;Wenzhen Huang;X. Huo;Yingqing Zhou
中科院分区：
文献类型：
--
作者：
K. Bastani;Z. Kong;Wenzhen Huang;X. Huo;Yingqing Zhou
尺寸完整性对多工位装配过程中最终产品的质量具有重大影响。人们提出了大量的故障诊断研究工作来确定产品尺寸变化较大的根本原因。这些方法基于产品的尺寸测量与可能的过程误差之间的线性关系，并假设测量的数量大于过程误差的数量。但在实际应用中，出于经济考虑，测量次数往往少于过程误差次数。这给多工位装配过程中的故障诊断带来了巨大的挑战，因为问题变成了求解欠定系统。为了应对这一挑战，提出了一种故障诊断方法，将状态空间模型与增强相关向量机（RVM）相结合，通过过程误差方差变化的稀疏估计来识别过程故障。案例研究的结果表明，所提出的方法可以成功识别过程故障。
Dimensional integrity has a significant impact on the quality of the final products in multistation assembly processes. A large body of research work in fault diagnosis has been proposed to identify the root causes of the large dimensional variations on products. These methods are based on a linear relationship between the dimensional measurements of the products and the possible process errors, and assume that the number of measurements is greater than that of process errors. However, in practice, the number of measurements is often less than that of process errors due to economical considerations. This brings a substantial challenge to the fault diagnosis in multistation assembly processes since the problem becomes solving an underdetermined system. In order to tackle this challenge, a fault diagnosis methodology is proposed by integrating the state space model with the enhanced relevance vector machine (RVM) to identify the process faults through the sparse estimate of the variance change of the process errors. The results of case studies demonstrate that the proposed methodology can identify process faults successfully.