Present manufacturing systems are equipped with sensors that provide a basis for real-time monitoring and diagnosis; however, placement of these sensors is constrained by the the functions that they perform and the system's structure. Moreover, sensors cannot be placed across all components in the equipment due to significant data challenges. This results in non-observable components, which limit our ability to support effective real-time monitoring and fault diagnosis initiatives. Consequently, product quality drifts found during inspection often result in unscheduled breakdown of all equipment involved in respective production operation. This situation becomes more complex for automated manufacturing lines, where success depends on our ability to capitalize maximum production capacities. This paper proposes a methodology that exploits historical data over unobserved equipment components to reduce the search space of potential faulty components, followed by a more accurate diagnosis of failures and causes. The purpose is to improve the effectiveness and efficiency of both the real-time monitoring of potential faulty components and the diagnosis of causes. In the proposed approach, we use a logical diagnosis approach to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network. The proposed approach computes the risk priority for suspected equipment with corresponding factors (such as human factor and recipe), using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins in real-time and support effective corrective maintenance decisions. The key advantages offered by this approach are: (i) reduced unscheduled equipment breakdown duration, and (ii) stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic methodology and can be deployed on fully or semi-automated manufacturing systems.
