Fault detection and isolation filter design for systems subject to polytopic uncertainties

This paper considers the robust fault detection and isolation (FDI) problem for linear time-invariant dynamic systems subject to faults, disturbances and polytopic uncertainties. We employ an observer-based FDI filter to generate a residual signal. We propose a cost function that penalizes a weighted combination of the deviation of the fault to residual dynamics from a given fault isolation reference model, as well as the effects of disturbances and uncertainties on the residual, using the Hinfin norm as a measure. The proposed cost function thus captures the requirements of fault detection and isolation and disturbance rejection in the presence of polytopic uncertainties. We derive necessary and sufficient conditions for the existence of an FDI filter that achieves the design specifications. This condition takes the form of easily implementable linear matrix inequality (LMI) optimization problem

Robust Fuzzy Observer-based Fault Detection for Nonlinear Systems

With the increasing demand for higher performance, safety and reliability of dynamic systems, fault diagnosis has received more and more attention. The observer-based strategy is one of the active research fields, which is widely used to construct model-based fault detection systems for technical processes which can be well modelled as linear time invariant systems. Fault diagnosis for nonlinear system is an active area of research. Observer-based fault detection includes two stages, residual generation and residual evaluation. The residual generation problems and residual evaluation problems for systems with only deterministic disturbances or stochastic disturbances have been widely separately studied. Recently some efforts have been made in the integrated design of fault detection systems for systems with deterministic disturbances and stochastic disturbances. Recently, successful results of applying Takagi-Sugeno (TS) fuzzy model-based technique to solve fault detection and isolation problems met in the nonlinear system have been achieved. With TS model, a nonlinear dynamic system can be linearised around a number of operating points. Each linear model represents the local system behaviour around the operating point. The global system behaviour is described by a fuzzy IF-THEN rules which represent local linear input/output relations of the nonlinear system. Applying the Takagi-Sugeno fuzzy model based technique to solve fault detection and isolation problems in the nonlinear systems is active area of research. The main contribution of this thesis is the design of robust fault detection systems based on Takagi-Sugeno fuzzy filters. There are a number of schemes to achieve robustness problem in fault detection. One of them is to introduce a performance index. It is function of unknown input signal and fault signal. For continuous time system, first, robust fault detection system will be designed for nonlinear system with only deterministic disturbance as unknown inputs. Second, robust fault detection system will be designed for nonlinear system with deterministic disturbance as unknown inputs and parameter uncertainties. Finally, robust fault detection system will be designed for nonlinear system with deterministic disturbance as unknown inputs and stated delay. Sufficient conditions for solving robustness problem are given in terms of Linear Matrix Inequalities (LMIs). For discrete time system, kalman filter design for nonlinear system is diffcult. In this thesis new fault detection approach will be presented for nonlinear system with only stochastic disturbance. Fault Detection (FD) system for each local subsystem is design by solving the corresponding Discrete-time Algebraic Riccati Equation (DARE). Optimisation algorithm based on minimizing the residual covariance matrix is used to obtain a robust FD system optimised for global system behaviour. The optimisation algorithm is established in terms of LMIs. The different robust fault diagnosis system are developed to detect sensor faults of vehicle lateral dynamic control systems

Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry

Autonomous robotic systems, such as quadrotors, are susceptible to actuator faults, and for the safe operation of such systems, timely detection and isolation of these faults is essential. Neural networks can be used for verification of actuator performance via online actuator fault detection with high accuracy. In this paper, we develop a novel model-free fault detection and isolation (FDI) framework for quadrotor systems using long-short-term memory (LSTM) neural network architecture. The proposed framework only uses system output data and the commanded control input and requires no knowledge of the system model. Utilizing the symmetry in quadrotor dynamics, we train the FDI for fault in just one of the motors (e.g., motor $\# 2$), and the trained FDI can predict faults in any of the motors. This reduction in search space enables us to design an FDI for partial fault as well as complete fault scenarios. Numerical experiments illustrate that the proposed NN-FDI correctly verifies the actuator performance and identifies partial as well as complete faults with over $90\%$ prediction accuracy. We also illustrate that model-free NN-FDI performs at par with model-based FDI, and is robust to model uncertainties as well as distribution shifts in input data.Comment: Accepted for 2023 Allerton Conference on Communication, Control, & Computin

A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

Fault detection and isolation filter design for systems subject to polytopic uncertainties

This paper considers the robust fault detection and isolation (FDI) problem for linear time-invariant dynamic systems subject to faults, disturbances and polytopic uncertainties. We employ an observer-based FDI filter to generate a residual signal. We propose a cost function that penalizes a weighted combination of the deviation of the fault to residual dynamics from a given fault isolation reference model, as well as the effects of disturbances and uncertainties on the residual, using the Hinfin norm as a measure. The proposed cost function thus captures the requirements of fault detection and isolation and disturbance rejection in the presence of polytopic uncertainties. We derive necessary and sufficient conditions for the existence of an FDI filter that achieves the design specifications. This condition takes the form of easily implementable linear matrix inequality (LMI) optimization problem

Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems

This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme

Modulating function based fault diagnosis using the parity space method

A model-based method for the detection and estimation of faults in dynamic systems is proposed. The method is based on the combination of the parity space approach and the modulating function framework for estimation. The parity space method is employed as an efficient geometric procedure determining null subspaces for annihilating unknown terms and formulating residuals. With the modulating functions technique the dynamic relation from output differentiation is reformulated as an algebraic expression. This substantially reduces the noise sensitivity of the output derivatives required. The design allows for the robust fault detection and isolation also for some nonlinear systems. The robustness of the approach is demonstrated on a nonlinear model of a four-tank process

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

A new strategy for integration of fault estimation within fault-tolerant control

© 2016 Elsevier Ltd. All rights reserved. The problem of active fault tolerant control (FTC) of dynamical systems involves the process of fault detection and isolation/fault estimation (FDI/FE) used to either make a decision as to when and how to change the control, based on FDI or to compensate the fault in the control system via FE. The combination of the decision-making/estimation and control gives rise to a bi-directional uncertainty in which the modelling and fault uncertainties and disturbances all affect the quality and robustness of the FTC system. This leads to the FTC requirement for an integrated design of the FDI/FE and control system reconfiguration. This paper focuses on the FTC approach using FE and fault compensation within the control system in which the design is achieved by integrating together the FE and FTC controller modules. The FE is based on a modified reduced-/full-order unknown input observer and the FTC system is constructed by sliding mode control using state/output feedback. The integrated design is converted into an observer-based robust control problem solved via H ∞ optimization with a single-step LMI formulation. The performance effectiveness of the proposed integrated design approach is illustrated through studying the control of an uncertain model of a DC motor

Fault detection and isolation in a networked multi-vehicle unmanned system

Recent years have witnessed a strong interest and intensive research activities in the area of networks of autonomous unmanned vehicles such as spacecraft formation flight, unmanned aerial vehicles, autonomous underwater vehicles, automated highway systems and multiple mobile robots. The envisaged networked architecture can provide surpassing performance capabilities and enhanced reliability; however, it requires extending the traditional theories of control, estimation and Fault Detection and Isolation (FDI). One of the many challenges for these systems is development of autonomous cooperative control which can maintain the group behavior and mission performance in the presence of undesirable events such as failures in the vehicles. In order to achieve this goal, the team should have the capability to detect and isolate vehicles faults and reconfigure the cooperative control algorithms to compensate for them. This dissertation deals with the design and development of fault detection and isolation algorithms for a network of unmanned vehicles. Addressing this problem is the main step towards the design of autonomous fault tolerant cooperative control of network of unmanned systems. We first formulate the FDI problem by considering ideal communication channels among the vehicles and solve this problem corresponding to three different architectures, namely centralized, decentralized, and semi-decentralized. The necessary and sufficient solvability conditions for each architecture are also derived based on geometric FDI approach. The effects of large environmental disturbances are subsequently taken into account in the design of FDI algorithms and robust hybrid FDI schemes for both linear and nonlinear systems are developed. Our proposed robust FDI algorithms are applied to a network of unmanned vehicles as well as Almost-Lighter-Than-Air-Vehicle (ALTAV). The effects of communication channels on fault detection and isolation performance are then investigated. A packet erasure channel model is considered for incorporating stochastic packet dropout of communication channels. Combining vehicle dynamics and communication links yields a discrete-time Markovian Jump System (MJS) mathematical model representation. This motivates development of a geometric FDI framework for both discrete-time and continuous-time Markovian jump systems. Our proposed FDI algorithm is then applied to a formation flight of satellites and a Vertical Take-Off and Landing (VTOL) helicopter problem. Finally, we investigate the problem of fault detection and isolation for time-delay systems as well as linear impulsive systems. The main motivation behind considering these two problems is that our developed geometric framework for Markovian jump systems can readily be applied to other class of systems. Broad classes of time-delay systems, namely, retarded, neutral, distributed and stochastic time-delay systems are investigated in this dissertation and a robust FDI algorithm is developed for each class of these systems. Moreover, it is shown that our proposed FDI algorithms for retarded and stochastic time-delay systems can potentially be applied in an integrated design of FDI/controller for a network of unmanned vehicles. Necessary and sufficient conditions for solvability of the fundamental problem of residual generation for linear impulsive systems are derived to conclude this dissertation