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

Model based fault diagnosis and prognosis of nonlinear systems

Rapid technological advances have led to more and more complex industrial systems with significantly higher risk of failures. Therefore, in this dissertation, a model-based fault diagnosis and prognosis framework has been developed for fast and reliable detection of faults and prediction of failures in nonlinear systems. In the first paper, a unified model-based fault diagnosis scheme capable of detecting both additive system faults and multiplicative actuator faults, as well as approximating the fault dynamics, performing fault type determination and time-to-failure determination, is designed. Stability of the observer and online approximator is guaranteed via an adaptive update law. Since outliers can degrade the performance of fault diagnostics, the second paper introduces an online neural network (NN) based outlier identification and removal scheme which is then combined with a fault detection scheme to enhance its performance. Outliers are detected based on the estimation error and a novel tuning law prevents the NN weights from being affected by outliers. In the third paper, in contrast to papers I and II, fault diagnosis of large-scale interconnected systems is investigated. A decentralized fault prognosis scheme is developed for such systems by using a network of local fault detectors (LFD) where each LFD only requires the local measurements. The online approximators in each LFD learn the unknown interconnection functions and the fault dynamics. Derivation of robust detection thresholds and detectability conditions are also included. The fourth paper extends the decentralized fault detection from paper III and develops an accommodation scheme for nonlinear continuous-time systems. By using both detection and accommodation online approximators, the control inputs are adjusted in order to minimize the fault effects. Finally in the fifth paper, the model-based fault diagnosis of distributed parameter systems (DPS) with parabolic PDE representation in continuous-time is discussed where a PDE-based observer is designed to perform fault detection as well as estimating the unavailable system states. An adaptive online approximator is incorporated in the observer to identify unknown fault parameters. Adaptive update law guarantees the convergence of estimations and allows determination of remaining useful life --Abstract, page iv

Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Decentralized sliding mode control and estimation for large-scale systems

This thesis concerns the development of an approach of decentralised robust control and estimation for large scale systems (LSSs) using robust sliding mode control (SMC) and sliding mode observers (SMO) theory based on a linear matrix inequality (LMI) approach. A complete theory of decentralized first order sliding mode theory is developed. The main developments proposed in this thesis are: The novel development of an LMI approach to decentralized state feedback SMC. The proposed strategy has good ability in combination with other robust methods to fulfill specific performance and robustness requirements. The development of output based SMC for large scale systems (LSSs). Three types of novel decentralized output feedback SMC methods have been developed using LMI design tools. In contrast to more conventional approaches to SMC design the use of some complicated transformations have been obviated. A decentralized approach to SMO theory has been developed focused on the Walcott-Żak SMO combined with LMI tools. A derivation for bounds applicable to the estimation error for decentralized systems has been given that involves unknown subsystem interactions and modeling uncertainty. Strategies for both actuator and sensor fault estimation using decentralized SMO are discussed.The thesis also provides a case study of the SMC and SMO concepts applied to a non-linear annealing furnace system modelderived from a distributed parameter (partial differential equation) thermal system. The study commences with a lumped system decentralised representation of the furnace derived from the partial differential equations. The SMO and SMC methods derived in the thesis are applied to this lumped parameter furnace model. Results are given demonstrating the validity of the methods proposed and showing a good potential for a valuable practical implementation of fault tolerant control based on furnace temperature sensor faults

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

On-line estimation approaches to fault-tolerant control of uncertain systems

This thesis is concerned with fault estimation in Fault-Tolerant Control (FTC) and as such involves the joint problem of on-line estimation within an adaptive control system. The faults that are considered are significant uncertainties affecting the control variables of the process and their estimates are used in an adaptive control compensation mechanism. The approach taken involves the active FTC, as the faults can be considered as uncertainties affecting the control system. The engineering (application domain) challenges that are addressed are: (1) On-line model-based fault estimation and compensation as an FTC problem, for systems with large but bounded fault magnitudes and for which the faults can be considered as a special form of dynamic uncertainty. (2) Fault-tolerance in the distributed control of uncertain inter-connected systems The thesis also describes how challenge (1) can be used in the distributed control problem of challenge (2). The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control action and the second acting as an adaptive compensation for significant uncertainties and fault effects. The fault effects are a form of uncertainty which is considered too large for the application of passive FTC methods. The thesis considers several approaches to robust control and estimation: augmented state observer (ASO); sliding mode control (SMC); sliding mode fault estimation via Sliding Mode Observer (SMO); linear parameter-varying (LPV) control; two-level distributed control with learning coordination

Robust model-based fault estimation and fault-tolerant control : towards an integration

To maintain robustly acceptable system performance, fault estimation (FE) is adopted to reconstruct fault signals and a fault-tolerant control (FTC) controller is employed to compensate for the fault effects. The inevitably existing system and estimation uncertainties result in the so-called bi-directional robustness interactions defined in this work between the FE and FTC functions, which gives rise to an important and challenging yet open integrated FE/FTC design problem concerned in this thesis. An example of fault-tolerant wind turbine pitch control is provided as a practical motivation for integrated FE/FTC design.To achieve the integrated FE/FTC design for linear systems, two strategies are proposed. A H∞ optimization based approach is first proposed for linear systems with differentiable matched faults, using augmented state unknown input observer FE and adaptive sliding mode FTC. The integrated design is converted into an observer-based robust control problem solved via a single-step linear matrix inequality formulation.With the purpose of an integrated design with more freedom and also applicable for a range of general fault scenarios, a decoupling approach is further proposed. This approach can estimate and compensate unmatched non-differentiable faults and perturbations by combined adaptive sliding mode augmented state unknown input observer and backstepping FTC controller. The observer structure renders a recovery of the Separation Principle and allows great freedom for the FE/FTC designs.Integrated FE/FTC design strategies are also developed for Takagi-Sugeno fuzzy modelling nonlinear systems, Lipschitz nonlinear systems, and large-scale interconnected systems, based on extensions of the H∞ optimization approach for linear systems.Tutorial examples are used to illustrate the design strategies for each approach. Physical systems, a 3-DOF (degree-of-freedom) helicopter and a 3-machine power system, are used to provide further evaluation of the proposed integrated FE/FTC strategies. Future research on this subject is also outlined

Fault Estimation Schemes of Wireless Networked Control Systems for Real-Time Industrial Applications

Bedingt durch das rasante Wachstum der Mikroelektronik sowie der Informations- und Kommunikationstechnologien wurde viel Aufmerksamkeit der Erforschung von drahtlos vernetzten Regelsystemen (W-NCS) gewidmet. Die Entwicklung der W-NCS schuf neue Herausforderungen für die Technologien zur Fehlerabschätzung (FE) bezüglich Störungen bei der Datenübertragung, wie zum Beispiel Übertragungsverzögerung, Paketverlust und Jitter. Um die Sicherheit und Zuverlässigkeit des Systems zu gewährleisten, ist die Entwicklung eines effektiven FE Ansatzes in vernetzten Systemen von zentraler Bedeutung. Andererseits sollten mit der Ausrichtung auf Anwendungen in der Echtzeit-Industrieautomatisierung die spezifischen Eigenschaften der Netzwerke angemessen berücksichtigt werden. Da die Aufgabe der Übertragung von Messungen und Steuerbefehlen in der Regel über einen Zeitraum deterministisch ist, sollten ein deterministischer Übertragungsmechanismus und die entsprechenden FE Verfahren vorgeschlagen werden. Motiviert durch die weit verbreitete Verwendung von sowohl zentralen und als auch dezentralen Strukturen in industriellen Prozesse, ist die Entwicklung sowohl von zentralen und als auch von dezentralen FE Methoden für W-NCS in der Industrieautomation das primäre Ziel dieser Arbeit. Diese Arbeit widmet sich zuerst der Modellierung der Prozesse und der Kommunikationsstruktur. Für die Modellierung der Kommunikation wird das Medium Access Control (MAC) Protokoll basierend auf dem Mehrfachzugriff im Zeitmultiplex (TDMA) modifiziert, um die Echtzeitfähigkeit zu gewährleisten. Das Prozessmodell wird unter Berücksichtigung der Abtastraten auf Basis der hierarchischen Struktur des W-NCS aufgestellt. Durch die Berücksichtigung der Unsicherheit von Netzwerken und Auswirkungen von Fehlern wird ein linear periodisches (LP) Systemmodell, durch die Integration des Kommunikationsmodells und des Prozessmodells, als Basis für die spätere Entwicklung präsentiert. Die weiteren Untersuchungen konzentrieren sich auf die Entwicklung von FE Modellen für zentrale und dezentrale W-NCS. Um eine erhöhte Robustheit gegen unbekannte Störungen und den Schätzfehler des Anfangszustandes zu erreichen, wird ein zentraler FE Ansatz mit Hilfe des stochastischen Modells im Krein Raum vorgeschlagen. Für die dezentrale FE wird der Algorithmus für jedes Teilsystem implementiert und die Kopplungsbeziehungen zwischen den Teilsystemen entsprechend berücksichtigt. Basierend darauf werden die FE Ansätze mit zwei Arten von Residuensignalen präsentiert, nicht-verteilten Residuen und verteilten Residuen,. Um die Wirksamkeit der entwickelten FE Ansätze darzustellen, wird in dieser Arbeit die Industrieplattform WiNC, zumammen mit einem Dreitanksystem verwendet. Die FE Algorithmen wurden in den drei Datenübertragungsfällen Fehlerfrei, mit Verzögerungen und mit Paketverlust verifiziert, so dass die Robustheit gegenüber einer unvollkommenen Kommunikation demonstriert wird. Darüber hinaus wurde die Leistungsfähigkeit bezüglich Sensor- und Aktuator-FE ausgiebig auf der WiNC Plattform getestet.With the rapid growth of microelectronics, information and communication technologies, much attention has been paid on the research of wireless networked control systems (W-NCSs). The development of W-NCSs raises new challenges in fault estimation (FE) technology regarding to the imperfect data transmission, such as transmission delay, packet loss, jitter and so on. To ensure the system safety and reliability, an effective FE approach over networks is of prime importance to be developed. On the other hand, aiming for the applications on real-time industrial automation, the specific characteristics of network should be properly considered. Since the transmission tasks of measurements and control commands are normally deterministic over a period of time, a deterministic transmission mechanism and the relevant FE scheme should be proposed. Motivated by the widespread popularity of centralized and decentralized structures for industrial processes, development of both centralized and decentralized FE schemes for W-NCSs, which can be applied on industrial automation, is the primary objective of this thesis. This thesis is first dedicated to the modeling of communication and process. For the communication modeling, time division multiple access (TDMA) based medium access control (MAC) protocol is modified to guarantee the real-time performance. The process model is built considering multirate sampling based on the hierarchical structure of W-NCSs. By observing the uncertainty of networks and effects of faults, a linear periodic (LP) system model, which is the integration of communication model and process model, is presented as a basis for the later developments. The further study focuses on the development of FE schemes for both centralized and decentralized W-NCSs. To reach an enhanced robustness against unknown disturbance and initial state estimate error, the centralized FE approach is proposed with the help of stochastic model in Krein space. For decentralized FE, the algorithm is implemented by every sub-system, and the coupling relations between sub-systems should be properly considered. Based on it, the FE approaches are presented with two kinds of residual signals, i.e., non-shared residuals and shared residuals, respectively. To illustrate the effectiveness of the derived FE approaches, an industrial platform WiNC integrated with three-tank system is utilized in this thesis. The FE algorithms have been verified for three data transmission cases, i.e., sampling-based, delay and packet loss, so that the robustness against imperfect communication is demonstrated. Moreover, the performances of sensor and actuator FE have also been tested well on WiNC platform