The cyber-physical systems (CPS) are characterized by a high degree of complexity due to the presence of networked heterogeneous components. This complexity makes it crucial to prevent error propagation in the system. Therefore, error detection and mitigation are necessary requirements in CPS. Recently, DL-based techniques have emerged as popular solutions for error detection in CPS. However, the main concern of DL-based error detection models in power constrained CPS is the trade-off between accuracy and speed. This leads to the necessity of designing optimized, accurate, and lightweight models.This paper proposes an optimized lightweight error detection model based on prediction approach. The paper addresses the limitations of conventional DL-based approaches in error detection for hardware-constrained CPS, particularly an exoskeleton system. The model adopted state-of-the-art efficient architecture that comprises in parallel CNN and LSTM layers, which is then transformed into a lightweight network through data quantization and network pruning techniques. The effectiveness of the proposed method is demonstrated through its application in error detection of the exoskeleton system’s data.

Modern Cyber-Physical Systems are characterized by dynamic and complex structure. They are facing factors such as erroneous software update, Artificial Intelligence components, reconfigurable structure, etc. Because of that, they are prone to latent or dormant faults. With the growth of data that needs to be processed, traditional error-handling mechanisms are failing to be efficient enough. For this reason, Deep Learning methods come into play. Application of such methods for detection and handling of error is nowadays a vastly used approach. However, the development of Deep Learning-based error detectors is time-consuming because there is no general approach to automatically exploit the specifics of the given system. In order to effectively address this issue, we propose a new hybrid deep learning-based methodology called Remedy. It comprises three steps: (1) identification of critical fault parameters, (2) automatic search for efficient access points for training data generation, and (3) deployment of deep learning-based error detector. To illustrate the effectiveness of this methodology, we present an implementation example on a classical Simulink model. Comparing the results with the corresponding baseline, the authors discuss the advantages and disadvantages of the proposed method.

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 per-form 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.

The current trend in production systems development is shifted towards the software part. This is emphasized by the concepts of digital twins and Software-Defined Manufacturing (SDM). These software-intensive and safety-critical systems have more frequent software updates to address higher system flexibility and adjustable production processes. SDM-systems bring new challenges to the reliability assessment. Each update can change the system behavior significantly. This leads to the necessity to reconduct reliability assessment automatically before each software update.In this paper, we introduce the concept of an automated model-based reliability assessment method sensible to the software update. The paper describes the key idea of the method and demonstrates its application on a model of a robotic manipulator.