Monitoring and diagnosis of process faults and sensor faults in manufacturing processes

The substantial growth in the use of automated in-process sensing technologies creates great opportunities for manufacturers to detect abnormal manufacturing processes and identify the root causes quickly. It is critical to locate and distinguish two types of faults – process faults and sensor faults. The procedures to monitor and diagnose process and sensor mean shift faults are presented with the assumption that the manufacturing processes can be modeled by a linear fault-quality model. A W control chart is developed to monitor the manufacturing process and quickly detect the occurrence of the sensor faults. Since the W chart is insensitive to process faults, when it is combined with U chart, both process faults and sensor faults can be detected and distinguished. A unit-free index referred to as the sensitivity ratio (SR) is defined to measure the sensitivity of the W chart. It shows that the sensitivity of the W chart is affected by the potential influence of the sensor measurement. A Bayesian variable selection based fault diagnosis approach is presented to locate the root causes of the abnormal processes. A Minimal Coupled Pattern (MCP) and its degree are defined to denote the coupled structure of a system. When less than half of the faults within an MCP occur, which is defined as sparse faults, the proposed fault diagnosis procedure can identify the correct root causes with high probability. Guidelines are provided for the hyperparameters selection in the Bayesian hierarchical model. An alternative CML method for hyperparameters selection is also discussed. With the large number of potential process faults and sensor faults, an MCMC method, e.g. MetropolisHastings algorithm can be applied to approximate the posterior probabilities of candidate models. The monitor and diagnosis procedures are demonstrated and evaluate through an autobody assembly example.