Foundations of Real Time Predictive Maintenance with Root Cause Analysis

Research on cyber-physical systems comes to the fore with the increasing progress of applications in the field of autonomous systems. Therefore, there is a growing interest in methods for enhancing reliability, availability, and self-adaptation of such systems in safety critical situations. Hence, it is essential that autonomous systems are equipped with a detection system to observe faulty behaviour in real time or to predict failing operations to avoid safety critical scenarios, which may harm people. To bring or hold a system within healthy conditions, not only detecting a faulty behaviour is important, but also to find the corresponding root cause. In this article, we introduce different methods which make use of detecting unexpected behaviour in cyber-physical systems, for the localization of faults. The first approach, model-based diagnosis uses logic to represent a cyber-physical system to perform reasoning for computing diagnosis candidates. A second promising approach deals with simulation-based diagnosis systems, using digital twin models to produce faulty behaviour data in advance, and to find correlations with the original cyber-physical system's behaviour, for diagnosis. For the third method the focus is set on artificial intelligence (machine learning and neural networks), where the goal is to utilize a huge amount of health and safety critical observations of the system for training to approximate the behaviour associated with faulty and safety critical states.