Supporting maintenance of variant-rich automated production systems by tracing of variable signal paths in electrical CAD

Variant-rich automated production systems oppose an increasingly difficult challenge. As they become more and more unique, not even skilled maintenance experts of the manufacturer have a sufficient understanding of the machine in the context of debugging and safety checks anymore and waste time by tracing signal paths within a system. Conventional methods from model-based systems engineering require significant effort to create and validate models before any automated influence analysis is possible. Therefore, in this article, we present a novel method and algorithms that extract all necessary information for signal path tracing directly from existing schematics from electrical engineering and easily reusable annotations to overcome these challenges. First, requirements are derived from interviews conducted with industrial experts. Based on those design restrains, a partial ECAD data model is derived and useful information is identified. Missing information is added by the development of a dedicated modeling language. By application to one lab and three industrial machines using a prototypical implementation of the concept, benchmarks and experts evaluate the applicability and benefits of the approach, as a deep understanding of signal flow within a machine is key to efficient testing and debugging. Therefore, in this article, we present a novel method and algorithms that extract all necessary information for signal path tracing directly from existing schematics from electrical engineering and easily reusable annotations to overcome these challenges. First, requirements are derived from interviews conducted with industrial experts. Based on those design restrains, a partial ECAD data model is derived and useful information is identified. Missing information is added by the development of a dedicated modeling language. By application to one lab and three industrial machines using a prototypical implementation of the concept, benchmarks and experts evaluate the applicability and benefits of the approach, as a deep understanding of signal flow within a machine is key to efficient testing and debugging.