Using Explicit and Machine-Understandable Engineering Knowledge for Defect Detection in Automation Systems Engineering

Today the costs of a failure in operation of huge industrial complexes are very high. Traditional approaches for defect detection in automation systems engineering in principle work, but generally don't take into account the semantic heterogeneity of tools and data models which are used within the engineering of industrial automation systems. Thus, some defects can remain undetected. Also, such systems have to be implemented anew for each concrete case. In this paper, the authors present their ongoing and planned research aimed to improve the defect detection processes.