FIDGET: Deep Learning-Based Fault Injection Framework for Safety Analysis and Intelligent Generation of Labeled Training Data.

Since the introduction of the term Cyber-Physical Systems (CPS) in 2006, they came to a long way. CPS are now autonomous and networked systems of systems with state-space exceeding the capabilities of conventional risk analysis methods. Model-based fault injection methods allow assessment of a system's fault tolerance not only during its design phase but also in the course of operation. This allows the evaluation of updates and new modules before deploying such changes to a real system. Such operational model-based fault injection on a system's digital twin can ensure continuous safety throughout all system life cycles. Modern risk analysis tools and Machine Learning-based safety methods require vast amounts of representative input and training data. Such methods not only will require mountains of erroneous time-series data from a myriad of operational cycles, but also corresponding fault parameter labels. As the state space of the system component explodes in complexity, it becomes problematic to cover all possible component fault combinations. As such, only those faults that could lead to potential failures or increased risk scenarios are of interest for automated safety assessment methodologies. It is clear that an intelligent and effective model-based fault injection method is required for the operational safety assessment of industrial CPS. Recently we introduced a new model-based fault injection method implemented as a highly customizable Simulink block called FIBlock. It supports the model-based injection of typical faults of CPS components such as sensors, software, computing, and network hardware. In this paper, we proposed a Deep Learning-based approach for model-based fault injection called FIDGET. It extends the FIBlock with Deep Reinforcement Learning capabilities. We employed a Deep Deterministic Policy Gradient algorithm with Long Short-Term Memory (LSTM) architecture to train the Reinforcement Learning agent to perform the automated search of fault parameters that yield the biggest system response. It allows automatic generation of labeled training data for further use in risk analysis tools or to train fault classifiers. The generated training data consists of errors that lead to the biggest response (i.e., disturbance) of the system.