Application of Bayesian networks for diagnostics in the assembly process by considering small measurement data sets

The ability of variation source diagnosis in the auto body assembly process plays an essential role in the success of the manufacturing enterprises. However, it is more challenging to identify the process faults associated with the compliant sheet metal parts based on small measurement data sets. A new Bayesian networks (BN) modeling approach under the condition of small data sets is proposed. The main causal links are identified based on mapping of the variation sensitivity matrix. The interaction effects are detected according to the conditional mutual information tests. After the network structure is determined, the Bayesian approach is used to obtain the conditional probability tables by incorporating prior probability distributions. The evaluation of diagnostic performance concerning evidence number and log-odds noise levels is also presented. A real bracket assembly case was used to illustrate the whole procedures for fixture fault diagnosis. The examined test cases demonstrate the proposed BN approach is practical and effective, even when incomplete evidences are observed and a medium-level noise is present.