Fault Estimation Method Research Articles

Simultaneous interval estimation of actuator fault and state for a class of nonlinear systems by zonotope analysis

In this paper, an actuator fault and state interval estimation method for a class of nonlinear systems is proposed by integrating observer design and zonotope analysis. For the considered systems, we present a novel unknown input observer structure with broad applications. The design procedure is based on H∞ method to decrease the influence of unknown but bounded process disturbances and measurement noise. Moreover, a novel interval estimation method is presented based on zonotope analysis to obtain tighter intervals. Numerical simulations of a quadruple-tank system are conducted to assess the performance of the proposed approach.

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.

Fault estimation for a class of nonlinear distributed systems using the adaptive observer technique

AbstractIn this article, a fault estimation method is proposed for a class of nonlinear distributed systems, in which the adaptive observer technique is exploited to estimate the parameters related to the fault in the interconnection items. First, we consider the simple case that the interconnection items only contain measurable variables. And then the result will be extended to the case that the interconnection items contain unmeasurable variables. Due to the existing of nonlinear item, the state and parameter estimations cannot be decoupled from each other, which makes the analysis of the convergence of the state and parameter estimations much more difficult. To overcome this problem, by designing of an integral Lyapunov function, and by exploiting the converse theorem of Lyapunov, a sufficient condition for the exponential convergence of the proposed algorithm is derived. Moreover, the detail relationship between the observer gain, Lipschitz parameter of the nonlinear item and the PE level of the estimation system is also derived. Simulation results show the effectiveness of the proposed fault identification scheme.

Sensor fault estimation based on ellipsoid bundles for dynamic point-the-bit rotary steerable systems

The dynamic point-the-bit rotary steerable system (DPRSS) achieves high-performance trajectory control in oil and gas exploitation. However, the sensor faults significantly degrade the reliability of the DPRSS. In this paper, the problem of sensor fault estimation for the DPRSS is investigated. Firstly, the DPRSS is modeled by a descriptor system with unknown but bounded uncertainties, which include measurement noises, model perturbations, and unknown inputs. Next, by integrating the decoupling technique, a sensor fault estimator is put forward based on the ellipsoid bundles. Besides, order reduction and bound calculation methods are proposed to derive intervals of the states and faults. Then, with a defined minimization criterion of the ellipsoid bundle, a sufficient condition is presented to design the parameters of the estimator. Finally, numerical simulations and experiments on a DPRSS prototype are provided to demonstrate the effectiveness of the proposed fault estimation method.

An estimation method for sensor faults based on observer in interconnected systems.

In this research, a class of nonlinear interconnected systems with sensor faults were investigated and an estimation method was proposed for system sensor faults based on the theory of system state reconstruction. Considering sensor fault vectors in nonlinear interconnected systems, this method constructed a generalized nonlinear interconnected system, whose state was designed by augmenting the original system state and fault vectors, which provides a foundation for fault estimation of nonlinear interconnected systems. An augmented observer was developed by equivalent transformation of generalized interconnected system, so as to realize robust estimations of sensor faults in interconnected systems. This estimation method took into account the effect of external disturbance of the system on fault estimation and estimated the convergence speed of error system; the developed method also considered the convenience of solving the gain matrix of the augmented observer, which was beneficial to the realization of sensor fault estimation in interconnected system. The sensor estimation method proposed in the paper has the advantages of robustness in fault estimation,rapidity in error convergence, and convenience in solving the gain matrix. Finally, the state and sensor fault estimation errors of two interconnected systems can both approach 0 within 10 seconds, thus achieving the purpose of fault estimation. Two simulation experiments verified the effectiveness of the proposed fault estimation method and provided a reference for the fault estimation method of nonlinear interconnected systems with disturbance.

Hybrid finite‐time fault‐tolerant consensus control of non‐linear fractional order multi‐agent systems based on fault detection and estimation

AbstractThis paper addresses the problem of achieving finite‐time fault‐tolerant consensus control for a class of non‐linear fractional‐order multi‐agent systems (NFO‐MAS) using finite‐time fault detection and estimation, as well as a finite‐time state observer. To achieve this, a specific lemma is utilized to rewrite the high‐order model of NFO‐MAS as a lower‐order NFO unique system. By employing new identification rules and introducing a fault estimation method, both the state variables and faults of the agents are estimated within a finite time. Subsequently, a finite‐time sliding mode control law is designed based on the estimated fault and the state variables obtained from the proposed finite‐time observer to achieve consensus within a finite time for the fractional‐order non‐linear MAS. The stability of the fault estimation, state observer, and consensus controller is proven using the finite‐time Lyapunov theory. The effectiveness of the proposed approach is demonstrated through numerical simulations.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Fault-Estimation Design Based on an Iterative Learning Scheme for Interconnected Multi-Flexible Manipulator Systems with Arbitrary Initial Value

This paper reports the design of an iterative-learning-scheme-based fault-estimation method for interconnected nonlinear multi-flexible manipulator systems with arbitrary initial value. For state estimation, observers are employed to reconstruct the state. The proposed scheme ensures that each flexible manipulator subsystem’s states can track their desired reference signals within a finite time. In the next step, an iterative learning fault-estimation law is proposed to track the actual fault signal. In contrast to the previous literature, this approach utilizes potential information from previous iterations to enhance the accuracy of the estimation in the current iteration. Based on these efforts, the obstacle caused by the arbitrary initial value is circumvented, and addressing the fault-estimation errors of each flexible manipulator subsystem are uniformly ultimately bounded is successfully achieved. Then, the λ-norm is developed to explore the convergence conditions of the presented methods. Finally, the effectiveness and feasibility of the proposed approach are verified through assessment of simulation results.

Fault detection and identification for rolling mill main drive system based on integrated observer under iterative learning strategy

In this article, the problem of multiple fault detection, isolation and reconfiguration of the rolling mill main drive system containing external disturbances is investigated. Considering the nonlinear frictional damping between the rolls and the rolled parts, a nonlinear mathematical model of the main drive system of the mill is established. A comprehensive fault diagnosis scheme based on observer is addressed for this system subjected to unknown external interference. The proposed scheme is divided into two parts. In the first stage, a set of sliding mode observers is designed for system fault detection, and a fault isolation criterion is proposed based on observer redundancy and generalised residual set theory to reveal the fault source. In the second stage, combined with the iterative learning algorithm, an iterative learning-unknown input observer is constructed to realise the accurate estimation of the fault signal. Unlike the existing fault estimation methods, the iterative learning-unknown input observer designed in this article uses the state estimation error of the previous iteration to estimate the fault signal in the current iteration period. Using [Formula: see text] synthesis to design observers for the system will guarantee fault diagnosis robustness. The Lyapunov theory and linear matrix inequality are introduced to prove the convergence of the proposed observer. The simulation study of a 1780-mm hot strip mill evaluates the proposed scheme. Simulation results demonstrate that the sliding mode observer approach can detect faults in the main drive system and isolate faults accurately. In contrast, the iterative learning-unknown input observer method has the lowest fault reconfiguration error (99.87% smaller than the extended state observer, 99.77% smaller than the unknown input observer) and achieves accurate fault signal tracking.

Fault estimation for nonlinear parameter-varying time-delayed systems

A fault estimation method for a class of nonlinear parameter-varying systems subject to time-varying delay and unmeasured nonlinearities is presented. The unmeasured time-varying parameters are effectively handled using a sector-based condition approach. A gain-scheduling intermediate estimator is proposed to simultaneously estimate the system state and the unknown faults. Design conditions are derived based on Lyapunov–Krasovskii functional and integral inequality techniques. These conditions, expressed as linear matrix inequalities, ensure that the estimation error dynamics are input-to-state stable with respect to the time-derivative of the faults. Moreover, it is demonstrated that for the case of piecewise constant faults, the estimation error dynamics are exponentially stable. As a corollary result, conditions are also presented to design gain-scheduling intermediate estimators for nonlinear parameter-varying systems without time-varying delays. Three physically motivated examples are provided to demonstrate the effectiveness and practical interests of the proposed nonlinear estimation method.

Fixed-time sliding mode observer-based multi-faults estimation method and its application to PMSM drive system

An integral sliding mode observer-based fixed-time multi-faults estimation method is proposed to solve the problem of fast fault estimation for a class of nonlinear systems with multiple types of faults simultaneously. First, a class of nonlinear systems with actuator and sensor faults is considered, and the original system is transformed into two subsystems through coordinate transformation to decouple the actuator and sensor faults. Then, intermediate variables are introduced to transform sensor faults into actuator faults of the augmented systems. For the transformed system, a fixed-time fault observer based on integral sliding mode is designed to realize the fast estimation of unknown terms, and the fixed-time convergence performance is proved. Moreover, based on the output of the fault observer, the actuator and sensor faults in the original system are reconstructed. Finally, the fault model of the permanent magnet synchronous motor is established in the hardware-in-the-loop platform, and the effectiveness of the designed fault estimation method is verified.

Convex Fault Diagnosis of a Three-Degree-of-Freedom Mechanical Crane

This paper presents a fault detection and estimation method based on a proportional-integral observer applied to a three-degree-of-freedom mechanical crane. Faults are common in this system and can provoke oscillations that generate a loss of performance and stability. A convex linear parameter varying approach is proposed to stabilize the crane and detect and isolate actuator faults to guarantee the crane’s performance. The linear matrix inequalities obtained from candidate Lyapunov functions give sufficient conditions to guarantee the fault estimation method. Finally, numerical simulations are proposed to illustrate the method’s performance and applicability.

A distributed fault estimation method for a class of nonlinear Markov jump systems

This paper investigates the distributed fault estimation problem of a class of nonlinear Markov jump systems. The main goal is to design a distributed observer network to estimate system states and actuator fault simultaneously. For the two cases of completely known and general uncertain transition rates, we first use a virtual intermediate observer technique to remove the observer matching condition, which upgrades the flexibility in the process of design. Next, by using the information of some special neighboring nodes and adopting observability decomposition approach, a novel distributed observer network allowed to be with unobservable nodes is constructed. Then, the mentioned goal is achieved based on the observer network. The proposed method can compensate missing information of unobservable nodes, which solves the estimation problem in the case of partial information loss. Finally, an example is provided to demonstrate the effectiveness of the proposed method.

Fault estimation and self-healing control of discrete-time T-S fuzzy model with sensor and actuator faults based on dual observers

This paper proposes a sensor and actuator fault estimation and self-healing control scheme to address issues related to deteriorating effluent quality, process failures, and safety hazards. Firstly, a discrete T-S fuzzy model of the wastewater treatment system is developed by employing the weighted recursive least squares method, which involves identifying the system based on the Benchmark Simulation Model No. 1. Secondly, in order to overcome the conservativeness problem when estimating sensor and actuator faults with a single observer, a fault estimation method employing dual observers is proposed utilizing the discrete-time T-S fuzzy model established. Thirdly, a fuzzy weighted self-healing controller is designed based on integral sliding mode and state feedback. This controller aims to overcome the aforementioned issues. Finally, Some simulations are provided to ensure the effectiveness of the obtained results. The simulation results show that compared with other methods, the fault estimation method using dual observers achieves shorter error estimation correction time and faster response to sudden fault estimation. Compared with output feedback controller, dynamic output feedback based self-healing controller exhibits superior control performance and better dynamic tracking performance.

A sensor fault detection, isolation, and estimation method for intelligent vehicles

Accurate and reliable sensor information is crucial for intelligent vehicles to ensure safety and reliability. It is significant to devise effective solutions to diagnose possible sensor faults. In this paper, a sensor fault detection, isolation, and estimation approach is developed for intelligent vehicle-integrated motion systems. The Luenberger observer is designed and embedded into the fault diagnosis framework to real-time estimate the system states. Interval observers are constructed for vehicle subsystems to estimate the interval bounds of the corresponding observation errors. The estimation errors enter into the corresponding intervals generated by the adaptive laws when the sensor fault is free. In the event of a sensor fault, it can be detected efficiently and the faulty sensor can be isolated. Moreover, occurred sensor fault can be estimated by modifying the interval observer structure. The simulation results in the standard J-Turn test scenario are provided to evaluate and verify the effectiveness of the proposed method.

Fault Isolation and Estimation in Networks of Linear Process Systems.

Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered in this paper with only a single conserved extensive quantity but with a network structure containing loops. These loops make fault detection and isolation challenging to perform because the effect of fault is propagated back to where it first occurred. As a dynamic model of network elements, a two input single output (2ISO) LTI state-space model is proposed for fault detection and isolation where the fault enters as an additive linear term into the equations. No simultaneously occurring faults are considered. A steady state analysis and superposition principle are used to analyse the effect of faults in a subsystem that propagates to the sensors' measurements at different positions. This analysis is the basis of our fault detection and isolation procedure that provides the position of the faulty element in a given loop of the network. A disturbance observer is also proposed to estimate the magnitude of the fault inspired by a proportional-integral (PI) observer. The proposed fault isolation and fault estimation methods have been verified and validated by using two simulation case studies in the MATLAB/Simulink environment.

A New Residual Generation-Based Fault Estimation Approach for Cyber-Physical Systems

This article investigates the problem of data-driven actuator fault estimation for cyber-physical systems (CPSs). First, a stable kernel representation (SKR)-based residual generator is constructed to achieve online detection for actuator fault using the system inputs–outputs (I/O) data. Then an iterative learning fault estimation (ILFE) algorithm is designed to reconstruct the actuator fault in real time. The proposed method realizes the actuator fault estimation when the model parameters are unknown, which avoids much time spent in the modeling process. Moreover, compared with the existing model-based approaches, the presented method still has better transient performance, steady-state performance, and computational efficiency, which is more suitable for the fault identification of practical control systems. Finally, the application to a servo motor demonstrates the superiority of the designed fault estimation method.

Fault Estimation Method for Nonlinear Time-Delay System Based on Intermediate Observer-Application on Quadrotor Unmanned Aerial Vehicle.

In this paper, the problem of actuator and sensor faults of a quadrotor unmanned aerial vehicle (QUAV) system is studied. In the system fault model, time delay, nonlinear term, and disturbances of QUAV during the flight are considered. A fault estimation algorithm based on an intermediate observer is proposed. To deal with a single actuator fault, an intermediate variable is introduced, and the intermediate observer is designed for the system to estimate fault. For simultaneous actuator and sensor faults, the system is first augmented, and then two intermediate variables are introduced, and an intermediate observer is designed for the augmented system to estimate the system state, faults, and disturbances. The Lyapunov-Krasovskii functional is used to prove that the estimation error system is uniformly eventually bounded. The simulation results verify the feasibility and effectiveness of the proposed fault estimation method.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

Plug-and-Play Robust Distributed Fault Estimation for Interconnected Systems

In this paper, a robust distributed fault estimation method is studied for interconnected systems with process and measurement disturbances. Each subsystem is monitored by an unknown input observer (UIO) based fault estimator, using local output measurement and estimated states of neighbors. The existence conditions of the distributed fault estimators (DFEs) are given in a decentralized fashion. Then, robust DFEs are designed to constrain the influence of the disturbances on the fault estimation errors, of which the global design conditions are transformed into a linear matrix inequality (LMI). Particularly, if the completely decoupling conditions are satisfied, the DFEs have an asymptotic estimation feature, and can be constructed using only local and interconnected information. Furthermore, the proposed robust DFEs are allowed for plug-and-play operations, and the corresponding conditions are analyzed in detail. Simulation studied on two cases is conducted to illustrate the effectiveness of the proposed method.