Fault localization for intelligent automation systems

Conventional programming of explicit control code is unsuitable for flexible and collaborative production systems. A model-based approach, which focuses on defining capabilities of a system, instead of specifying how to achieve them, provides an alternative for creating complex, scalable, and reliable systems. This is accomplished through the use of behavior models, and tools such as planning, synthesis, verification, and testing. However, developing such models is not without challenges, as it is possible to overlook or incorrectly specify potential behavior and constraints. This can result in unsolvable planning problems or plans that are invalid for other reasons. When plans are unobtainable, developers receive no feedback, which makes model adjustments a difficult and time-intensive task. This paper recognizes these challenges as crucial barriers for adopting model-based development of intelligent automation systems. To facilitate the development of such systems, an approach for detecting and localizing faults in behavior models is presented. Drawing inspiration from software fault localization techniques, the proposed method involves identifying suspicious resources, variables, and operations. The effectiveness of this approach is illustrated with an example use case.