Fault Estimation Research Articles

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

Model‐based fault diagnosis for safety‐critical chemical reactors: An experimental study

AbstractThis article presents an experimental application of fault detection, isolation, and estimation in a chemical reactor system, introducing a functional observer‐based approach without the need for linear approximation. The residual signal generators, functioning as disturbance‐decoupled functional observers, provide fault size estimates and enable fault isolation through multiple generators operating independently. The experimental study focuses on the 3‐Picoline oxidation process, deriving a discrete‐time model, and constructing specific residual generators for coolant inlet temperature and feed concentration faults. Fault diagnosis employs Fast Fourier Transform (FFT) filtering and Generalized Likelihood Ratio (GLR), facilitating on‐the‐fly detection during the experiment. The effectiveness of fault detection, disturbance decoupling, and estimation is experimentally validated.

Adaptive fault-tolerant control for spacecraft: A dynamic Stackelberg game approach with advantage actor-critic reinforcement learning

This study introduces an innovative approach that harnesses the Stackelberg differential game framework in combination with advantage actor-critic (A2C) reinforcement learning (RL) to attain adaptive and dynamic control, in the presence of complex faults within spacecraft systems. By reconfiguring the problem of fault tolerance into a strategic interaction involving two pivotal players, specifically, the leader (fault-tolerant control, FTC) and the follower (fault detection observer, FDO), our approach facilitates the dynamic adaptation of their control strategies in response to the system's state, effectively providing a solution for addressing faults within spacecraft systems. This approach overcomes the challenges of cross-optimal indexes commonly found in traditional FTC, resulting in improved efficiency and stability of RL. It harbors substantial potential for applications requiring robust and adaptive FTC strategies. Simulation validation, both the leader and follower can successfully control the spacecraft's velocity before and after a fault occurs, ultimately achieving optimal fault estimation (FE) and FTC objectives.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

Command filter–based fixed-time fault estimation and compensation control for nonlinear systems with prescribed performance

Command filter–based fixed-time fault estimation and compensation control for nonlinear systems with prescribed performance

Unknown input observer design for uncertain nonlinear systems with time-varying delay: application to online fault estimation

This paper recommends an unknown input observer (UIO) for uncertain nonlinear time-delay dynamics that enables online fault estimation. The main novelty of this paper is designing UIO to fault estimation for nonlinear systems that include time-varying delays in states and time-varying uncertainties in all matrices of the system. The other advantage of the proposed method is that both abrupt and incipient faults are considered. Furthermore, the state and output disturbances, as well as actuator and sensor faults, are considered. The Lyapunov–Krasovskii concept is used to guarantee the stability of the augmented closed-loop system. Calculating observer gains and stabilisation of estimation error dynamics are accomplished by utilising a new linear matrix inequality (LMI). Finally, an uncertain continuous-stirred tank reactor (CSTR) with a time-varying delay is used to evaluate the performance of the suggested technique.

Adjustable order fault estimation observer based fault-tolerant control for switched fuzzy stochastic systems

Based on fault estimation, fault-tolerant control problem of switching stochastic nonlinear systems with actuator fault and sensor fault are studied. The fuzzy models are invited to solve the nonlinear problems of the systems. The fault estimation observer is designed to estimate the system states, actuator and sensor faults. The order of observer can be adjusted, which can improve the design flexibility and realize a compromise between the estimated cost and estimated accuracy. Unlike some classical results, the output differential is not required in the observer, which increases the application range. Based on the observer, the fault-tolerant controller is proposed, besides, the sufficient condition is achieved to ensure that the closed-loop system is mean-squared exponential stability. Finally, the feasibility is verified by two simulations.

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

Iterative‐learning‐based actuator/sensor fault estimation for a class of repetitive nonlinear systems with time‐delay

AbstractTo meet the demand for estimating actuator and sensor faults simultaneously in a class of repetitive nonlinear time‐delay systems, this paper proposes a novel fault estimation strategy based on an iterative learning scheme. Firstly, an iterative‐learning‐based fault estimation law is designed to estimate actuator faults while system is free of sensor failures. Both the fixed initial shift and random one are taken into consideration. Secondly, a novel sensor fault observer is proposed based on an augmented state variable which consists of original system state and sensor fault signal; output compensation strategy is also provided to ensure the iterative‐learning‐based actuator fault estimation method is effective considering the existence of sensor failures. In addition, theorems based on ‐norm and linear matrix inequality are provided to determine values or ranges of gain matrices and parameters in proposed sensor fault observer and iterative‐learning‐based actuator fault estimation law. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.

Spatiotemporal fault estimation for switched nonlinear reaction–diffusion systems via adaptive iterative learning

SummaryIn this paper, an iterative learning‐based spatiotemporal fault estimation issue in switched reaction–diffusion systems is investigated. Initially, average dwell‐time switching rules are utilized to describe a class of switched reaction–diffusion systems characterized by mode jumps. Then, different from the existing fault estimation methods, a fault estimator is designed for spatiotemporal faults to realize an accurate estimation of faults by using the iterative learning strategy. Subsequently, to improve the speed of fault estimation, an adaptive iterative learning‐based fault estimation law is proposed, which can achieve faster fault estimation by continuously adjusting the iterative learning gain. Moreover, sufficient conditions for the convergence of the fault estimation error are obtained by using the ‐norm and the mathematical induction methods. Finally, an illustrative example is presented to check the practicality and superiority of the proposed fault estimation scheme.

Active Adaptive Composite Fault Tolerant Controller Design For Nonlinear Systems

Abstract It can be seen from the public opinion monitoring that there are many cases of death and injury caused by quality problems of mechanical products, which poses a serious threat to people’s lives. Therefore, it is particularly important to analyze the reliability of mechanical products. This study focuses on active adaptive composite fault tolerant controller (ACFTC) design for nonlinear system with actuator fault. Firstly, a novel iterative learning observer (ILO) is proposed to estimate the early potential fault. And then, the ACFTC approach is proposed in order to obtain desired performance in the faulty case based on the fault estimation information from ILO. Finally, simulation of the flexible joint robot system verifies the validity and applicability of the algorithm.

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

Active fault-tolerant control for asynchronous machines using EKF-based fault estimation and 3-H-bridge inverter mitigation of ITSCs

This paper introduces an active fault-tolerant control system designed to effectively detect and mitigate inter-turn short-circuit (ITSC) faults in asynchronous machines. Utilizing the extended Kalman filter, the system precisely estimates electrical parameters, including resistances and inductances, crucial for identifying such faults. By integrating these estimations into the control mechanism, the system dynamically detects, isolates, and adjusts control laws based on fault severity assessments. The proposed controller, built upon an accurate three-phase faulty model, not only addresses torque ripple issues but also incorporates a 3-H-bridge inverter to manage current imbalances, particularly in degraded operating conditions. To evaluate its efficacy and robustness, comprehensive numerical simulations were conducted using MATLAB/SIMULINK. Results validate the effectiveness of the proposed control approach in ensuring the uninterrupted safe operation of asynchronous motors, even in the presence of ITSC faults, presenting a promising solution for enhancing fault tolerance in industrial settings. Further exploration is warranted to assess its real-world applicability and potential limitations across diverse operational scenarios. This system’s implementation promises practical benefits by bolstering operational reliability and safety in industrial machinery, contributing to improved workforce safety and reduced downtime, thus offering significant social advantages.

Improvement of local outlier factor algorithms for lithium-ion battery fault diagnosis

Fault diagnosis is one of the most important active strategies to protect lithium-ion batteries (LIBs) from safety accidents. The tasks of fault diagnosis usually can be divided into three levels, i.e., (1) fault detection, (2) fault isolation, and (3) fault estimation. The local outlier factor (LOF) method has been proved effective in conducting fault detection (level 1 of fault diagnosis) for LIB energy storage systems (ESSs). However, the original LOF algorithm (OLOF) may fail to make correct fault estimation (level 3 of fault diagnosis). Therefore, in this study, two novel LOF algorithms, i.e., the improved LOF algorithm (ILOF) and the simplified LOF algorithm (SLOF), are put forward to enhance the capability of fault estimation. The performance of the three LOF algorithms: OLOF, ILOF and SLOF, are firstly compared via three mathematical cases. Their capabilities in fault diagnosis are further investigated based on the simulated data from an air-cooled LIB ESS and the experimental data from a water-cooled LIB ESS. Results prove the effectiveness of all the three algorithms in fault detection; nevertheless, when the OLOF algorithm fails to make correct fault estimation, both of the two proposed novel algorithms, i.e., the ILOF algorithm and the SLOF algorithm, present the capability in making correct fault estimation. It is found as well that the ILOF algorithm is better than the SLOF algorithm in robustness though the latter is more efficient than the former in memory usage and computational time.

Distributed observer for fault estimation in Load-Frequency Control of multi-area power systems with communication delays

This paper proposes an innovative approach to designing a distributed observer for fault and state estimations in Load Frequency Control (LFC) of time-delayed multi-area interconnected power systems. To reach this goal, the paper first provides exhaustive information and analysis on the structure, disturbances, and faults in multi-area power systems. Then, an L1 observer with the capability to simultaneously estimate the system’s states, process/actuator faults, and sensor faults is proposed. This observer exhibits robustness against various types of disturbances as well as time delays present within both the system and the communication network. The sufficient conditions for designing the proposed observer, guaranteeing the system stability, and minimizing the L1 performance index, are expressed as optimization problems employing Linear Matrix Inequality (LMI) techniques. Additionally, the effectiveness and credibility of the proposed distributed observer in estimating all states and faults in all areas are validated through simulation results.

Safe tracking control for unmanned autonomous helicopter under actuator faults and outside disturbances

This paper considers the problem on tracking control for the medium-scale unmanned autonomous helicopter (UAH) system under external disturbances, actuator faults, and full state constraints. In comparison with existing results, this paper does not only consider asymmetric state constraints but also jointly take the faults and disturbances into account on the UAH system, estimating and compensating for them separately, which can effectively enhance the tracking performance. First, the dynamics of nonlinear UAH system is divided into the position subsystem and the attitude one. Second, an improved barrier Lyapunov function (BLF) is constructed to handle the problem of asymmetric state constraints. Third, in order to estimate the external disturbances and actuator faults, two coupled generalized proportional integral observers (GPIOs) and two fault estimators are designed, respectively. Fourth, based on above estimations, improved BLFs, and backstepping method, two tracking control laws for the position and attitude loops are presented and an overall closed-loop system is established. Then, a sufficient condition ensuring uniform boundedness of tracking and estimation errors is derived. Finally, some simulating results demonstrate the effectiveness and advantage of the proposed control scheme.

Event‐triggered resilient joint mobile robot localization and sensor fault estimation

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Adaptive neural event-dependent intermittent fault-tolerant control of reaction–diffusion multi-agent systems

This article employs an event-dependent intermittent fault-tolerant control approach to address the adaptive consensus of reaction–diffusion multi-agent systems under directed topologies in both leaderless and leader-following networks. Initially, an event-dependent intermittent control mechanism is proposed to determine the work/rest time, relying on three partitions of the non-negative real region and the formulated Lyapunov function. Subsequently, under the introduced control mechanism, an adaptive consensus protocol is established, incorporating the updating law for adaptive coupling strength and adaptive weight estimation. Utilizing the neural network estimation theorem for fault estimation, the article presents explicit criteria to ensure the attainment of leaderless or leader-following consensus in reaction–diffusion multi-agent systems. Finally, two numerical simulation experiments are conducted to validate the robustness and effectiveness of the derived theorem.

An Adjustable Parameter-Based Robust Distributed Fault Diagnosis for One-Sided Lipschitz Formation of Clustered Multi-Agent Systems

This paper addresses the challenge of distributed fault diagnosis in the context of the one-sided Lipschitz formation of agents. Each agent integrates an observer to detect and estimate both linear and non-linear faults in its attitude control subsystem. A robust design configuration is also developed to account for external perturbations. The robust observer utilized in this study is an unknown input observer (UIO), designed to mitigate the impact of disturbances on fault and state estimation errors. The observer’s parameters are determined using linear matrix inequalities (LMIs). Furthermore, a UIO incorporating an adjustable parameter (AP) is introduced to enhance fault diagnosis accuracy. Simulation results for two satellite clusters, consisting of five satellites with varying dynamics due to external disturbances, are presented to validate the approach. Instead of equipping every agent with an observer, specific agents can be equipped with observers to detect faults throughout the constellation, thereby reducing computational demands in configurations with numerous agents. Finally, a comparison is made between the proposed AP-based UIO and a standard UIO. The comparison findings reveal a noteworthy average of a substantial 56.61% reduction in root mean square error (RMSE) employing AP-based UIO compared to the utilization of standard robust UIO.

Fault estimation for a class of nonlinear algebro-differential parameter-varying systems

This paper presents a methodology to simultaneous estimate state variables and actuator fault for descriptor nonlinear parameter-varying systems. This class of systems is composed of an interpolated parameter-varying linear part and Lipschitz nonlinear one. The proposed observer structure allows some robustness and steady state accuracy in face to parametric uncertainties. The conditions of existence and stability are given in terms of LMIs (Linear Matrix Inequalities). The obtained results are illustrated on the heat exchanger system with two countercurrent cells model with actuator fault.