Diagnosability study of multistage manufacturing processes based on linear mixed-effects models

Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of a minimal diagnosable class is proposed to expose the "aliasing" structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can be used for any general linear input-output system, is illustrated using a panel assembly process and an engine head machining process.