Distributed fault detection and isolation in second order networked systems in a cyber-physical environment

Privacy-enhanced momentum federated learning via differential privacy and chaotic system in industrial Cyber-Physical systems

By leveraging Industrial Cyber-Physical Systems (ICPSs), deep learning-based methods are applied to address various industrial issues. Due to privacy policy reasons, conventional centralized learning (CL) may be improper for some industrial scenarios with sensitive data, such as smart medicine. Recently, federated learning (FL) as a novel collaboration learning approach has received extensive attention, which can break data barriers between different institutions to improve the model performance. However, the privacy information of the industrial agents may be inferred from their shared parameters. In this paper, we propose a Privacy-Enhanced Momentum Federated Learning framework, named PEMFL, that amalgamates differential privacy (DP), Momentum FL (MFL) and chaos-based encryption method. During the training, differentially privacy is used to disturb the industrial agents' gradient parameters in order to preserve their privacy information. Meanwhile, each industrial agent uses the chaos system-based encryption method to encrypt the weight parameters of their local models, which has two advantages: (1) the encryption method can enhance privacy protection; (2) the cloud server cannot access the truth value of the global model parameters which is a vital asset to the industrial agents. In addition, Momentum Gradient Descent (MGD) and an adjusting learning rate schedule are adopted to improve training efficiency for the PEMFL. The performance of the PEMFL is evaluated based on two non-i.i.d datasets. Theoretical analysis and experimental results demonstrate the excellent performance of the PEMFL in terms of accuracy and privacy security.

Design of a computationally efficient observer-based distributed fault detection and isolation scheme in second-order networked control systems

This paper presents a novel directional observer-based fault detection and isolation scheme for second-order networked control systems (NCS). The directional unknown input observer (UIO) tool is exploited to study the problem of distributed fault detection and isolation (FDI). Two design schemes with global and partial/local network models are proposed to solve the distributed FDI problem. Thresholds are computed for the application of the proposed schemes in a noisy environment. In addition, the salient features of the proposed schemes are that both fault detection and fault isolation are achieved in a single step using a single observer. The schemes are applied to power system models to validate their results. A detailed comparison with existing FDI schemes is also provided, which clearly shows the effectiveness of the proposed scheme in terms of computational requirements.

A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.