With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

In recent years, a significant interest in hybrid and battery electric vehicles has arisen globally due to reducing fuel consumption, mitigating dependence on imported oil and decreasing greenhouse gas emissions. The overall success of these vehicles mostly depends on the performance of sub-systems that they are created. In order to enhance the performances of these sub-systems, estimation of their parameters with high accuracy is required. Furthermore, estimation strategies play an important role in battery management, vehicle energy management and vehicle control by undertaking different tasks. There have been a limited number of review studies related with estimation strategies that are only focused on battery state of charge (SOC) and state of health (SOH) estimation. This paper presents a comprehensive review on various estimation strategies used in hybrid and battery electric vehicles for the first time in the literature. The existing estimation strategies are classified and different methodologies used in each estimation strategy are elaborated. Recent research advances on existing estimation strategies are clearly emphasized by reviewing numerous studies over 200 papers.

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Motivated by several accidents, attitude control of a spacecraft subject to faults/failures has gained considerable attention in a wider range of aerospace engineering and academic communities. This paper is concerned with industrial practices and theoretical approaches for fault tolerant control (FTC) and fault detection and diagnosis (FDD) in spacecraft attitude control system. An overview on recent development of spacecraft attitude FTC system design is presented. The basis of a FTC system is introduced. The existing engineering FTC techniques and theoretical methodologies, including their advantages and disadvantages, are discussed. Moreover, closely associated with the reliability-relevant issues, recent progress in attitude FTC design strategies is reviewed. A brief review of some open problems in the general area of spacecraft attitude control design subject to components faults/failures is further concluded.

A Review on Recent Development of Spacecraft Attitude Fault Tolerant Control System

This survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic non-linear dynamical modelling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided.

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Model-based fault diagnosis for aerospace systems: a survey:

Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO-based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.

An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems

Lithium-ion batteries possess high power, energy, and long cycle life. They are best candidates for applications on hybrid and electric vehicles. To ensure reliable operation, one of the functions in a battery management system is health monitoring in terms of fault diagnosis and estimation. The purpose of this brief is to provide a fault-diagnostic scheme that can achieve single fault isolation and estimation for a three-cell battery string subject to uncertainties. Detecting and isolating faults in systems subject to uncertainties is a challenging task due to the difficulty in distinguishing the effects of faults from uncertainties. To facilitate fault isolation, a bank of systems, each corresponding to a particular fault, are formulated by reorganizing the considered system. Each system in the bank is first transformed into two subsystems. Then, a classical Luenberger observer is designed for the first subsystem to generate a fault-detection residual. In this manner, a bank of reduced-order Luenberger observers are designed to locate a specific fault source, and thus fault isolation is achieved. Parallel to the bank of reduced-order Luenberger observers, a bank of learning observers (LOs) are also constructed to provide an estimate of the isolated fault. As a result, the synthesized design of Luenberger observers and LOs can realize simultaneous fault isolation and estimation. Parameters of an A123 battery cell are extracted via experiments, and effectiveness of the proposed design is demonstrated through simulation studies on the model of a three-cell battery string.

Simultaneous Fault Isolation and Estimation of Lithium-Ion Batteries via Synthesized Design of Luenberger and Learning Observers

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

Fault detection, estimation and compensation problem for a class of disturbance given time delay nonlinear systems is addressed. The proposed approach relies on an iterative learning observer (ILO) for fault detection and estimation. Under no faults, the ILO supplies accurate disturbance estimation where the effect of disturbances on estimation error dynamics can be accordingly attenuated. The proposed ILO can detect sudden changes in the nonlinear system. Thus the ILO is used to excite an adaptive control law in order to offset the effect of faults on the system dynamics. In addition, the ILO-based adaptive fault compensation strategy can handle multiple faults. The fault detection and compensation strategy is demonstrated in simulation on an automotive engine problem.

Fault detection and accommodation in nonlinear time-delay systems

This paper presents a general framework for fault detection and accommodation using iterative learning strategy. An iterative learning observer (ILO) updated online by past system output errors as well as input is constructed for the purpose of fault detection. This observer is different from conventional Luenberger observer where the observer's state is only a function of the most recent input, output and the estimation error. Furthermore, using iterative learning (IL) approach, an automatic control reconfiguration scheme for accommodation of faults is also described. One of the main features of the proposed scheme is that the control reconfiguration is achieved automatically based only on the response of the overall systems, and the IL controller does not require a fault detection and isolation subsystems. An example is employed to verify the effectiveness of the IL observer and IL fault accommodation scheme.

Wen Chen

Papers

An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems

Simultaneous Fault Isolation and Estimation of Lithium-Ion Batteries via Synthesized Design of Luenberger and Learning Observers

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

Fault detection and accommodation in nonlinear time-delay systems

An iterative learning observer-based approach to fault detection and accommodation in nonlinear systems