Shai A. Arogeti

Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives

Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between systems variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of systems behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.

Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation

This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid systems fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.

Brief On controllability and trajectory tracking of a kinematic vehicle model

This paper presents some further results concerning the issues of controllability and trajectory tracking regarding a front-wheel drive vehicle kinematic model. A simple procedure for computing an open-loop control strategy that transfers the system between given initial and final states, is presented. In particular, the input function is computed by means of a set of linear algebraic equations. The resulting motion planning procedure allows us to present a control scheme for solving the trajectory (a time-parameterized reference signal) tracking problem. Various applications of the approach in forward and backward motions are considered, and simulation results are presented.

Model-based Health Monitoring of Hybrid Systems

This book systematically presents a comprehensive framework and effective techniques for in-depth analysis, clear design procedure, and efficient implementation of diagnosis and prognosis algorithms for hybrid systems. It offers an overview of the fundamentals of diagnosis\prognosis and hybrid bond graph modeling. This book also describes hybrid bond graph-based quantitative fault detection, isolation and estimation. Moreover, it also presents strategies to track the system mode and predict the remaining useful life under multiple fault condition. A real world complex hybrid systema vehicle steering control systemis studied using the developed fault diagnosis methods to show practical significance. Readers of this book will benefit from easy-to-understand fundamentals of bond graph models, concepts of health monitoring, fault diagnosis and failure prognosis, as well as hybrid systems. The reader will gain knowledge of fault detection and isolation in complex systems including those with hybrid nature, and will learn state-of-the-art developments in theory and technologies of fault diagnosis and failure prognosis for complex systems.

Fault Detection Isolation and Estimation in a Vehicle Steering System

Recently, a bond-graph-based fault detection and isolation (FDI) framework has been developed with a new concept of global analytical redundancy relations (GARRs) (Low, Wang, Arogeti, and Luo, 2009, 2010; Low, Wang, Arogeti, and Zhang, 2010). This new concept allows the fault diagnosis for hybrid systems which consist of both continuous dynamics and discrete modes. A failure of a safety critical system such as the steering system of an automated guided vehicle may cause severe damage. Such failure can be avoided by an early detection and estimation of faults. In this paper, the newly developed FDI method is studied in details using an electrohydraulic steering system of an electric vehicle. The steering system and faults are modeled as a hybrid dynamic system by the hybrid bond graph (HBG) modeling technique. GARRs are then derived systematically from the HBG model with a specific causality assignment. Fault detection, isolation, and estimation are applied, experimental setup is described, and results are discussed.

A simple velocity-free controller for attitude regulation of a spacecraft with delayed feedback

In this note, we consider the application of a velocity-free controller for attitude regulation of a rigid spacecraft with gas jet actuators when the effects of time-delays in the feedback loop are taken into consideration. Simple sufficient conditions for exponential stability are presented. Some structural properties of the resulting closed-loop system are studied, and relevant design tools are demonstrated.

Mode Identification of Hybrid Systems in the Presence of Fault

A mode identification method for hybrid system diagnosis is proposed. The method is presented as a module of a quantitative health monitoring framework for hybrid systems. After fault occurrence, the fault is detected and isolated. The next step is fault parameters estimation, where the size of the fault is identified. Fault parameter estimation is based on data collected from the hybrid system while the system is faulty, and its dynamical model is partially unknown. A hybrid systems dynamics consists of continuous behavior and discrete states represented by modes. Fault parameter estimation requires knowledge of the monitored systems operating mode. The new method utilizes the partially known dynamical model to identify hybrid system modes in the presence of a single parametric fault.

Causality assignment and model approximation for quantitative hybrid bond graph-based fault diagnosis

Abstract Bond graph (BG) is an effective tool for modeling complex systems and it has been proven to be useful for fault detection and isolation (FDI) purposes for large continuous systems. BG provides causality between systems variables which allows FDI algorithms to be developed systematically from the graph. Similarly, Hybrid bond graph (HBG) is a bond graph-based modeling approach which provides an avenue to model complex hybrid systems; however, due to the lack of understanding, HBG has not been well-utilized for fault diagnosis. This is the first of a two-part paper that investigates the feasibility of utilizing HBG for quantitative FDI applications for hybrid systems. In this first paper, we present an analysis on the causality properties of the HBG where useful properties and insights associated with FDI applications are gained. Based on these properties, a causality assignment procedure and modeling approximation techniques are developed to achieve a HBG with a causality that facilitates efficient and effective FDI design for hybrid systems.

Monitoring Ability Analysis and Qualitative Fault Diagnosis Using Hybrid Bond Graph

Abstract In part I of this work, we lay a foundation for quantitative bond graph-based FDI design of hybrid systems using hybrid bond graph (HBG). We discussed the causality properties of HBG from FDI perspectives, and proposed the concept of Diagnostic Hybrid Bond graph (DHBG) which is advantageous for efficient and effective FDI applications. Part II presents a continuation of our previous paper [1]. In this part II of our work, we exploit the DHBG to analyze the systems fault monitoring ability. Additionally, we proposed a quantitative FDI framework for effective fault diagnosis for hybrid systems.

Fault parameter estimation for hybrid systems using hybrid bond graph

In this paper, a Hybrid Bond Graph based fault estimation is developed to estimate faulty parameters. This estimation method is based on a set of unified equations called the Global Analytical Redundancy Relations (GARRs). The developed estimation technique estimates fault parameters that can be linearly or nonlinearly parameterized, and it is formulated as a nonlinear least-square problem. This method is able to estimate the value of the fault parameters of a complex hybrid system even when a mode change occurs after the fault occurrence. Simulation results show that the fault parameters can be estimated effectively using the proposed technique.