Fault Estimation Research Articles

An Adaptive Fuzzy Approach to Fault Estimation Observer Design With Actuator Fault and Digital Communication.

This article investigates the fault estimation (FE) problem for a class of nonlinear systems via an adaptive fuzzy approach. Considering the limited communication capacity of networks, the quantized measurement signals are used to construct adaptive laws instead of the real measurements in the designed fuzzy observer. By injecting the quantizer parameter into the observer inputs, the quantization effects on the convergence of estimation errors can be compensated. It is also shown that nondifferentiable actuator faults can be reconstructed by the developed FE approach. Finally, two simulation examples are provided to illustrate the validity of the presented scheme.

Fault estimation and consensus tracking of multi-agent systems based on intermediate estimator

The consensus tracking of multi-agent systems (MASs) with process and sensor faults is investigated in this paper. First, process faults are taken into account, and an intermediate estimator is designed to estimate the system states and faults. By formulating appropriate Lyapunov functions, some sufficient conditions are derived to ensure the consensus tracking of MASs. Subsequently, the obtained results are extended to the case that both process and sensor faults occur in MASs. Leveraging the estimated system states and faults, an unique control protocol is proposed to achieve the consensus tracking of MASs and effectively compensate for process faults. The obtained theoretical results are demonstrated and verified by two simulation examples.

Distributed observer-based resilient control of islanded DC microgrids under actuator faults

In this paper, a distributed observer-based resilient control scheme is proposed to solve the voltage regulation and current sharing problems of islanded direct current (DC) microgrids under actuator faults. First, an observer-based distributed fault estimation design is presented to estimate fault signals with high accuracy. Then, based on the estimated information, a primary decentralized fault-tolerant control layer is designed to ensure accurate voltage reference tracking. A secondary cooperative control layer is employed to achieve the goal of current sharing. Besides, both the fault estimation observers and the fault-tolerant controllers are designed in a plug-and-play fashion, which guarantees the scalability of the DC microgrid. The simulation and experimental results show that the proposed control strategy can efficiently improve the reliability and resilience of the DC microgrid.

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.

Digital Twin Based on Historical Data and Simulation Results: Fault Detection and Estimation of the Remaining Useful Life of a Cyclone Bag Filter

This study deals with the development of a digital twin for monitoring the operating conditions of a cyclone bag filter installed on the suction system of a wheat mill. The model aims to be used for fault identification and real-time prediction of the remaining useful life (RUL). Computational fluid dynamics simulations were performed to characterize in detail the fluid-dynamic behavior of the airflow inside the system under different conditions of filter sleeve clogging. Furthermore, the simulation results were used to identify a location for the installation of a new velocity sensor that would allow, together with the pressure drop measured at the ends of the filter, monitoring of the systems’ conditions. A model able to assess the filter’s operating state, identify failure events or operating anomalies, and make a prediction of the RUL was then developed. A possible implementation of the developed model, based on the simulation results that aimed to optimize the management of the sleeve cleaning cycles was also proposed. The developed digital model was then tested on a working cycle lasting one year, in which a sleeve failure was simulated. It was shown how the simultaneous monitoring of the two identified quantities allows for the correct identification of the failure and the accurate prediction of the RUL.

Design of linear unknown input observers for sensor fault estimation in nonlinear systems

A systematic method for the design of linear unknown input observers (UIO) for sensor fault estimation in nonlinear systems is developed. Necessary and sufficient conditions for the existence of linear UIOs for nonlinear systems are derived. As long as these conditions are satisfied, we obtain explicit design formulas for the observer. The method is applied to a non-isothermal chemical reactor (CSTR) subject to a concentration sensor fault.

Unbiased state and fault estimation for discrete-time complex networks with time delays

In this paper, the joint state and fault estimation problem for a class of discrete-time complex networks with time delays is investigated. The information on the dynamics of the fault is not required with an appropriate structure of the proposed fault estimator. Unbiased state and fault estimation can be obtained simultaneously at each node only with locally available information, thereby avoiding the requirement of a centre node which collects information from every node. Considering the effects of time delays and the interconnections between different nodes, an upper bound of the estimation error covariance is calculated, and the desired filter parameters are obtained in a recursive manner such that the trace of the bound is minimised. The existence conditions of the developed filter are provided explicitly as well. Finally, a simulation example is employed to demonstrate the state estimation and fault diagnosis performances of the given strategy.

Incipient fault diagnosis and trend prediction in nonlinear closed-loop systems with Gaussian and non-Gaussian noise

This paper proposes a methodology for incipient fault diagnosis and the corresponding trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non-Gaussian uncertainties. The proposed approach is based on the use of the particle filtering technique for estimating the system states and outputs. From these estimations, the residual signals would be generated through a mathematical filtering and augmentation technique, allowing the incipient fault estimation that is evaluated using the designed fixed and adaptive thresholds that consider system uncertainties. In this way, the fault detection performance is improved but also the false detection and false alarm problems are comprehensively addressed. Moreover, the augmented Gauss–Newton identification method is used for the incipient fault trend prediction. Finally, to evaluate the effectiveness of the proposed approach, the incipient fault diagnosis in the heat transfer unit built in the nonlinear closed-loop continuous stirred-tank reactor (CSTR) system is used. Besides, the confusion matrix is employed to assess the results from a quantitative point of view.

Novel barrier Lyapunov function-based backstepping fault tolerant control system for an ROV with thruster constraints

This paper proposes a novel Barrier Lyapunov Function (BLF)-based fault tolerant control system for a work-class Remotely-Operated Vehicle (ROV) with thruster saturation and rate limits. The proposed control system is composed of a fixed-time state and fault observer, and a novel BLF-based backstepping controller. The novel BLF-based controller is introduced to eliminate the sensitivity of conventional BLF-based algorithms to rate limitations of the thrusters. In addition, it helps to improve the performance during initial transients and provides faster response to fault and failures. The observer combines the dynamics of the ROV with the dynamics of the thruster system. This combination facilitates estimation of thruster faults and failures independently of the system uncertainties, so highly improved performance is expected compared to conventional extended-state observers. To account for thruster constraints and provide fault tolerance, control allocation is utilized. Stability analysis is performed for the observer and controller, and it is shown that the state estimation error is fixed-time convergent, the fault estimation unit is bounded-input bounded-output stable, and the controller is exponentially stable. Simulations are carried out and comparisons are made with several asymptotic and finite-time BLF-based control systems. The simulations confirm the superior performance of the proposed control system.

A travelling wave-based fault locator for radial distribution systems using decision trees to mitigate multiple estimations

Electrical systems have been facing transformations, such as distributed generation insertion, system expansion and regulatory standards in order to increase reliability and quality of the power supply. Thus, fault location methods must be updated to ensure accuracy in estimating the location of electrical faults. The delay in restoring the system causes damage to utilities and consumers. Considering this, the current work presents an approach capable of locating faults accurately in radial distribution systems. At first, the distance is estimated using the travelling wave theory with data acquired from two terminals. Next, due to the radial characteristic of the system, the proposal aims to mitigate the problem of multiple estimation of faults. Thus, features are extracted from the voltage and current signals, which are used as inputs of decision trees to identify the fault region. The proposed approach was validated in a medium voltage distribution system, in which the results presented an average error of 0.79% (with a standard deviation of 0.4%) in estimating the fault distances and an average accuracy above 88.7% in identifying the region under fault. Thus, it was demonstrated that the proposed methodology is efficient to locate faults, mitigating the problem of multiple estimation.

Robust Fault Estimation and Fault-Tolerant Control for Discrete-Time Systems Subject to Periodic Disturbances

To enhance the reliability of digital automation systems in the Industry 4.0 era, this paper investigates a robust fault-tolerant control scheme in the discrete-time domain subject to periodic disturbances, consisting of a fault estimator, dynamic disturbance compensation loop, and fault-tolerant controller. The fault estimator simultaneously estimates both the system states and actuator/sensor faults. The existence and stability conditions of the proposed estimator are given, and a robust design method is proposed to make the state estimates robust to disturbances. To further reduce the estimation errors caused by periodic disturbances, a novel disturbance compensation loop is introduced and is optimized by a joint zero-assignment and pole-optimization method to delicately compensate for the adverse impacts of periodic input disturbances. The proposed robust fault-tolerant controller uses fault estimation to ensure fast recovery in the event of bounded actuator/sensor faults. The proposed scheme is evaluated through simulations of a two-wheeled mobile robot subject to periodic disturbances and simultaneous abrupt inclination angular sensor and ramp actuator faults, where its performance is shown to exceed that of existing methods.

A Novel Set-Valued Sensor Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

Sensor fault diagnosis is of great significance to ensure safe battery operation. This paper proposes a novel sensor fault diagnosis method that achieves the simultaneous fault detection, fault source and type identification, and fault estimation in a comprehensive way. Specifically, a set-valued observer, featuring a state predictor and a state estimator, is first constructed and designed to guarantee the inclusion of the unavailable actual battery state due to unknown modeling errors and noises at every instant of time. Compared with the traditional observers, a distinct feature of the proposed one lies in that the calculated state predictions and estimations of the battery system at each time step are ellipsoidal sets in state space rather than single vectors. The boundedness of state prediction and estimation errors is formally proved, and the tractable design criteria for determining the real-time optimal prediction and estimation ellipsoids are also derived. As for diagnosis algorithm, fault detection is implemented based on the intersection between the prediction and estimation ellipsoids. Then, a two-layer Pearson correlation coefficient analysis mechanism is developed to identify the source and type of sensor faults. Another set-valued observer based on an augmented battery model is further designed to estimate the fault level. Finally, experimental studies of a battery cell under different sensor fault sources, types and values are elaborated to verify the effectiveness of the proposed method.

Novel Technique for Location Identification and Estimation of Extent of Turn-to-Turn Fault in Transformer Winding

This paper presents a novel technique for diagnosing a turn-to-turn fault at the incipient level inside the power transformer winding using frequency response analysis (FRA). Utilizing lower frequency region of FRA in case of pre-fault and post fault conditions of transformer windings, the proposed technique determines the deviation in equivalent inductances. Based on the deviation, a location factor (<i>lf</i>) is derived, which indicates location of fault, by calculating accuracy factor (<i>x</i>) corresponding to each section of the winding. Thereafter, the extent of turn-to-turn fault and number of faulted turns are determined based on the accuracy factor of the faulted disk. The performance of the proposed technique has been validated through a simulation model as well as from two different laboratory-developed winding models and 400 kVA real filed transformer. The results designate that the developed technique is proficient to estimate location of turn-to-turn fault even when number of faulted turns are lower than 5&#x0025; of the total turns. Its response remains immune against winding configurations and lead error encountered during measurement. Eventually, a comparative assessment of the proposed approach with various other approaches indicates its eminence in terms of turn-to-turn fault localization and estimation of fault extent and faulted turns.

A novel design approach to state and fault estimation for interconnected systems using distributed observer

A novel design approach to state and fault estimation for interconnected systems using distributed observer

A Study of Software Defect Prediction Using Artificial Intelligent and Business Risk Management Approach

Abstract: The usage of several artificial intelligence algorithms in the area of software defect prediction is reviewed in the literature in this paper, it review the use of various techniques for artificial intelligence in the field of software defect prediction. Software system flaws remain a significant issue, and modern software development now faces many difficulties. Identification, estimation, and evaluation of the risks are crucial steps in order to avoid, minimize, and monitor the risks and their effects.This literature review is quite helpful because it improves understanding of the studied area. A software fault is a flaw in an executable product that results in software failure. Saving money, time, and effort may be accomplished by identifying general software process areas that may require attention from the start of a project and understanding the potential causes of problems. Planning, managing, and carrying out software development activities could be aided by the ability for early fault estimation. In order to study how many prediction approaches have been developed over this time to address the problems linked to software defect, this research conducts a literature review of works from the previous many years.In the field of software development, the study also discusses software risk management. This study makes a significant contribution by proposing a method for analyzing, organizing, and utilizing information technology initiatives in the context of software defect prediction and risk management.

Fault tolerant control for a class of nonlinear systems with multiple faults using neuro-dynamic programming

Considering the multiple fault scenario, i.e., both actuator and sensor faults occur simultaneously, this paper develops a neuro-dynamic programming (NDP)-based fault tolerant control (FTC) scheme for a class of nonlinear systems. By combining a descriptor observer with an adaptive observer, system states and multiple faults are estimated simultaneously. For the nominal system, i.e., the fault-free system, a critic neural network (NN) is employed to solve the Hamilton–Jacobi-Bellman (HJB) equation, and the approximate optimal control policy is obtained. To eliminate the influence of sensor faults, the accurate estimations of system states are used instead of system states from faulty sensors to construct the approximate optimal control policy. Then, by combining the estimations of actuator faults with the approximate optimal control policy, an FTC law is developed to suppress the influence of actuator faults. The stability of the closed-loop nonlinear system is analyzed to be uniformly ultimately bounded via the Lyapunov stability theorem. The effectiveness of the present FTC scheme is demonstrated by two simulation examples.

On the design of an unknown input observer to fault detection, isolation, and estimation for uncertain multi-delay nonlinear systems

In this article, an unknown input observer (UIO) is proposed for fault detection and isolation (FDI) for uncertain nonlinear time-delay systems with multiple discrete delays. The presented UIO can be able to isolate both actuator and sensor faults and estimate them to identify the shape, time of occurrence, amplitude, and other features of the faults. Furthermore, the proposed FDI method is robust to both input/state and output disturbances. At first, the system dynamics and faults are augmented and then UIO is driven. A delay-independent Lyapunov–Krasovskii functional is utilized to guarantee the stability of the proposed UIO-based FDI method. The terms of stability and UIO gains are obtained by solving some feasible linear matrix inequalities (LMI). The proposed method is simulated on two interconnected well-mixed non-isothermal continuous stirred tank reactors (CSTRs) with recycling where there are three parallel, irreversible elemental exothermic reactions. Two point-wise time delays in the output temperature and concentration in both CSTRs are considered. Furthermore, parametric uncertainties in the modeling are investigated. The simulation and numerical results show the superiority of the proposed UIO for fault detection, isolation, and estimation in the presence of multiple discrete time delays, model uncertainty, and input/state and output disturbances.

Descriptor sliding mode observer based fault tolerant control for nuclear power plant with actuator and sensor faults

In sophisticated and complex system such as nuclear power plant, fault estimation and fault tolerant control always play an important role in maintaining the system stability and assuring satisfactory and safe operation. Thus, in this work a fault estimation and fault tolerant control scheme based on sliding mode theory is proposed for a pressurized water reactor type nuclear power plant considering simultaneous actuator and sensor faults. First, using descriptor sliding mode observer approach, an accurate estimation of the system states and sensor fault vector have been obtained simultaneously. Then, based on the estimated information, an integral type sliding mode control scheme is proposed to stabilize the resulting faulty system. With the help of Lyapunov stability theory, reachabilities of the proposed sliding mode surfaces are shown in both the state estimation space and the error estimation space, simultaneously. Finally, the efficacy of the proposed control scheme is shown by applying it to a nuclear power plant.

Intermediate observer based simultaneous estimation of system states and multiple faults

AbstractIn this paper, an intermediate observer is proposed for simultaneously estimating the system state, actuator fault and sensor fault. The necessary and sufficient condition is given with respect to the existence of the designed observer. The observer gain matrix is guaranteed through solving Riccati inequalities. The estimation errors of the system state, actuator fault and sensor fault are proved to be globally uniformly stable with the prescribed performance index. Finally, a simulation study is given to verify the effectiveness of the proposed method.

Dynamic event-triggered fault estimation for complex networks with time-correlated fading channels

In this paper, the problem of dynamic event-based finite-horizon H∞ fault estimation is studied for a class of discrete-time complex networks with fading channels. In the communication from sensor to estimator, the channel fading phenomenon is considered, which is a dynamic process and reflects the actual situation. Moreover, the dynamic event-triggered mechanism is implemented to schedule the signal transmissions. This paper plans to design a fault estimator to meet the predetermined H∞ performance requirements. First, sufficient conditions are established to ensure the existence of the desired H∞ fault estimator. Then, the gain of the H∞ fault estimator is derived in terms of the solution to a set of recursive matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of our developed fault estimation approach.