Adaptive Event-Triggered Fault Detection for Fuzzy Stochastic Systems With Missing Measurements

This brief concerns the event-triggered fault detection problem for a class of networked nonlinear stochastic systems with randomly occurring dropouts and time-varying delays to guarantee the safety and reliability of systems. In order to save limited communication bandwidth, a novel adaptive memory-based event-triggered scheme (ETS) is developed, based on which a memory-based fault detection filter (FDF) is constructed to generate residual signal. Then, an event-triggered fault detection framework is established, which is mainly composed of stochastic system, ETS, FDF, fault weighting, and residual evaluation. With considering the ETS, random dropouts, and time-varying transmission delays, the criterion of mean-square asymptotic stability and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance is given such that the residual signal is sensitive to faults while robust against disturbance. Then, the co-design method of memory-based ETS and FDF is proposed. Finally, a simulation case is provided to demonstrate the effectiveness and advantages of the proposed method.

This paper is concerned with event-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for a class of networked Markov jump systems (MJSs) with missing measurements. The phenomenon of asynchronism occurs in both the controller and the actuator-failure model, which is estimated by the hidden Markov model, is taken into consideration. In addition, to reduce the burden of data transmission, a mode-dependent event-triggered mechanism (ETM) is proposed. Together with ETM, a network-induced delay is introduced. Subsequently, to guarantee that the MJS is stochastically stable, an event-based asynchronous controller is designed. Finally, to reveal the effectiveness of the proposed method, a simulation example of pulse width-modulation-driven boost converter is considered.

Despite intensive research being conducted on the topic of adaptive filter design in general, adaptive filter design in the discrete wavelet transform (DWT) domain with specific constraints is still an active research area. We have developed a method for the design of a 2-channel perfect-reconstruction adaptive wavelet filter which is optimized under minimum energy constraints in specific bands. The optimal 2-channel conjugate quadrature filter (CQF) bank has been designed using sequential quadratic programming techniques for nonlinear, nonconvex functions in general, although finding a global minimum is not guaranteed. However, such a filter can be effectively used with a wavelet based image encoder for high fidelity transmission of large image data sets at low bit rates.

Event-triggered fault detection filter design for nonlinear networked systems via fuzzy Lyapunov functions

Abstract. In this paper, possibilities of using fuzzy system models as a part of model-based fault diagnosis systems are discussed. Methods and experiences of using fuzzy system models at different stages of fault diagnosis systems are presented. In this study, fuzzy system models are used in fault detection and inverse fuzzy system models in fault localization. Inverse fuzzy models are discussed in the light of real-world applications. The mathematical theory is skipped entirely. Additionally we present some examples of using a fuzzy system model in an interface between a fault diagnosis system and a (human) operator. We also briefly discuss a total management of the life-cycle of mechatronic systems. We divide the life-cycle of a mechatronic system into four stages: design, development, installation (integration), and operation and maintenance. When a mechatronic device is being designed and built, it is difficult to analyze the behavior of the device before testing it in practice. It is common that many prototypes of the device are tested at different stages of the development process. The main idea of our approach is that a fuzzy model of a system is built as early as possible, and the model is used, for example, in analyzing the validity of design work done so far. The model is updated and used for different purposes throughout the development and installation process. Finally, the model is used as a part of fault diagnosis and control systems.Fault DiagnosisKnowledge Based SystemsFuzzy Modeling

This article focuses on the event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H\infty$</tex-math></inline-formula> control problem of the networked descriptor system with time delays. First, a novel event-triggered communication strategy is proposed, which can adaptively adjust the triggering parameters in light of dynamic errors. The expected control performance can be ensured in parallel with saving limited communication resources. Second, an integrated system model is established by considering network-induced delays, event-triggered strategy, and systems' time-varying delays. A co-design control method based on the event-triggered strategy is proposed by implementing the Lyapunov method. Finally, the effectiveness and advantages of the proposed method are presented through two examples.

Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

Novel adaptive fault detection method based on kernel entropy component analysis integrating moving window of dissimilarity for nonlinear dynamic processes

This paper is concerned with the event-triggered fault detection filter (FDF) design for an unmanned surface vehicle (USV) under the network environment. A framework of fault detection is established for a USV subject to wave-induced disturbance and actuator failures, in which an FDF is utilized to construct a residual model and an integral-based event generator is introduced to save communication resources. Compared with the traditional instantaneous value based event-triggering scheme and periodic sampling, the proposed event-triggering mechanism can not only reduce bandwidth utilization of the network more significantly, but also get rid of the Zeno phenomenon fundamentally. The event-triggering scheme and the FDF are co-designed. Finally, the efficient performance of the proposed fault detection method based on integral-based event-triggering scheme is illustrated by simulation.

To solve the problem of missing measurements in highly manoeuvring target tracking, an expected‐mode‐augmentation‐based unscented Kalman filter with missing measurements compensation (EMA‐MMCUKF) is designed based on the variable‐structure multiple model method. In the proposed EMA‐MMCUKF, the random missing measurements are described by the Bernoulli distribution, and the one‐step prediction is used as the compensation. Based on the proposed EMA‐MMCUKF, a Bayesian estimation‐based expected‐mode‐augmentation unscented Kalman filter with missing measurements compensation (BE‐EMA‐MMCUKF) is proposed for adaptive estimation of the sensor measurement reception rate, where the unknown measurement reception rate can be estimated by fully utilising prior information. Simulation results demonstrate that the proposed EMA‐MMCUKF can effectively track the manoeuvring target at different measurement reception rates. Moreover, when the sensor prior information differs significantly from the true measurement reception rate, the proposed BE‐EMA‐MMCUKF can effectively estimate the unknown sensor measurement reception rate and improve the accuracy of manoeuvring target tracking compared with non‐estimation of the sensor measurement reception rate.

This paper investigates the design of a robust fault detection observer for Wireless Networked Control Systems (WNCS) subject to random packet delays introduced by the wireless communication networks. The concept of input-delay analysis is adopted for analyzing the impacts of the packet delay on the residual generation, and a robust observer design and optimization method is proposed to improve the robustness of fault detection against the network induced delay. For the purpose of realistic simulation, a mixed simulation is carried out, where the control system is simulated in MATLAB and the network-induced delays are achieved by simulating an IEEE 802.11b wireless network at the recently developed OMNeT++ platform. The simulation results validate the proposed robust fault detection observer design approach.

Dynamic event-triggered security control of cyber-physical systems against missing measurements and cyber-attacks

Multi-modal image acquisition techniques have allowed digital images to penetrate domains from micro-scale medical imaging to mega-scale satellite imaging. For postprocessing, deep learning techniques have widely been used for image denoising and artifact suppression. However, far little work has been done to summarize their effectiveness concerning adaptive filter design, e.g., salt and pepper noise, stochastic Poisson, or additive white noise. Because different images, natural or urban, structured or unstructured scenes, and objects produce different types of noise, from the modality as well as from the imaging medium, devising a single method for all noise types is impractical. This paper proposes to use reinforcement learning (Q-learning) to adaptively design filters of a convolutional neural network (CNN). In contrast to the popular state of the art methods that use filter designs based on the noise model, CNN filters lack the power to do so. We have attempted to address this limitation of CNN by introducing a new modality of reinforcement learning for adaptive filter design. The qualitative and quantitative analysis of the proposed method is done and its efficacy is demonstrated using the following evaluation metrics: Peak Signal to Noise Ratio (PSNR), Contrast to Noise Ratio (CNR), and Structure Similarity Index (SSIM).

This paper presents a sliding mode-based adaptive fault detection and emergency control algorithm for implementation in fail-safe systems of autonomous vehicles. The overall algorithm is comprised of a fault detection part and a fail-safe control part. For the former, sliding mode observer-based fault detection algorithms were developed for environment and chassis sensors, including LiDAR, Radar, and acceleration sensors. Unidentified fault signals from the sensors are reconstructed through the adaptive sliding mode observer. The reconstruction is based on the MIT rule through the use of an estimated sensitivity parameter. For the latter, a sliding mode control (SMC)-based emergency control method designed to respond to fault occurrences has been proposed to ensure the functional safety of autonomous vehicles. An adaptive gain parameter was designed, taking convergence time into consideration, to secure consistent and rapid responses from the controller. When the detection algorithm detects a fault, the appropriate control input is computed by a lower controller for the vehicle. This control input is calculated based on the last scene information obtained from an upper controller. The performance of the proposed fault detection and control algorithms has been evaluated through simulations and actual vehicle tests of various scenarios.

Fault detection problem of networked control systems with network-induced delay, data packets dropout and disturbance in sensor-to-controller link is discussed. First, we design an observer-based residual generator, and model the residual dynamics to be a discrete-time markovian jump linear system in this paper. By using H ∞ optimization theory and linear matrix in-equation approach, the solution of alarm threshold is proposed. Then we introduce a new index to design a fault detection filter, which improves the performance of dynamic system, the sensitivity of the detection system to the fault. The approach of solving suitable observer gain matrix is proposed. At last, a numerical example is given to verify the effect of the method presented in this paper.