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Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of compliant sheet metal parts for the fixture fault diagnosis.

Based on the initial sensor locations and measurement data in launch time of the assembly process, the Bayesian network approach for fixture fault diagnosis is proposed to construct the diagnostic model. Furthermore, given the desired number of sensors, the diagnostic ability of the sensor set is evaluated based on the mutual information of the nodes. Thereby, a new sensor placement method is put forward and validated with a real automotive sheet metal part.

The new proposed method can be used to perform the fixture fault diagnosis and sensor placement optimization effectively, especially in a data‐rich environment. And it is robust in the presence of measurement noise.

This paper presents a novel approach for fixture fault diagnosis and optimal sensor placement in the assembly process.