Fault Diagnosis Of Hybrid Systems Research Articles

Sample-Evaluation-Enhanced Machine Learning Approach for Fault Diagnosis of Hybrid Systems

Sample-Evaluation-Enhanced Machine Learning Approach for Fault Diagnosis of Hybrid Systems

Fault diagnosis for hybrid systems based on a bank of linear observers and a discrete automaton

This paper presents a fault diagnosis approach for a four-tank system considered as a hybrid dynamical system. The proposed diagnosis approach is based on a hybrid observer composed of a discrete automaton and a bank of linear observers. An automaton is used to improve the performance of the proposed method. For this study, we considered sensor, parametric and discrete faults. Simulation results showed the efficiency of the proposed diagnosis approach to detect and to localize the considered faults, despite the hybrid behaviors of the studied system.

Fault Diagnosis in Nonlinear Hybrid Systems

Abstract The problem of fault diagnosis in hybrid systems is investigated. It is assumed that the hybrid systems under consideration consist of a finite automaton, a set of nonlinear difference equations and the so-called mode activator that coordinates the action of the other two parts. To solve the fault diagnosis problem, hybrid residual generators based on both diagnostic observers and parity relations are used. It is shown that the hybrid nature of the system imposes some restrictions on the possibility of creating such generators. Sufficient solvability conditions of the fault diagnosis problem are found. Examples illustrate details of the solution.

Modeling and robust fault diagnosis of hybrid system based on hybrid bond graph approach

In this article a method for robust Fault Detection and Isolation FDI is presented The scientific interest of this work is use of one tool Bond Graph not only for modelling of hybrid system parametric and measurement uncertainties but also for generation of robust fault indicators and thresholds For this task first the hybrid bond graph HBG model is established to model the hybrid system using the controlled junctions Secondly all parametric and measurement uncertainties are modeled on the HBG model in derivative causality from this model and using the causal and structural properties of the bond graph tool the generation of the fault indicators residues and thresholds is done directly those fault indicators called Generalized Analytical Redundancy Relations GARRs are valid at all modes and derived systematically from an HBG model An application to an hydraulic is used to illustrate this method

Fault Diagnosis in Hybrid Systems by Nonparametric Method

The problem of fault diagnosis in hybrid systems is investigated. The hybrid systems under consideration consist of finite automaton, the set of nonlinear difference equations and so-called mode activator that coordinates the action of these two parts. The hybrid residual generator based on the nonparametric method is found that solves the fault diagnosis problem under certain (sufficient) solvability conditions. Examples illustrate details of the solution.

Online Monitoring and Fault Diagnosis of Hybrid Systems Using Switched Dynamic Bayesian Networks


 
 
 Modern real-world engineering systems typically have hybrid dynamic behaviors that can be modeled by continuous behaviors with discrete mode transitions. These complex systems present many significant challenges for online monitoring and diagnosis, including tracking system behavior, dealing with noisy measurements and disturbances, and diagnosing different types of faults. In this paper, we propose an integrated model-based diagnosis approach that extends the traditional Dynamic Bayesian Network-based particle filter approach for tracking continuous system dynamics. A novel mode diagnoser is presented that discriminates between residuals generated by inaccurate system tracking, discrete faults, and parametric faults. An extended quantitative fault isolation and identification scheme is combined with a qualitative fault isolation scheme to identify the abrupt parametric faults. We demonstrate the effectiveness of our approach by applying it to Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS) developed at the NASA Johnson Space Center for long duration human missions.
 
 

An Efficient Model-based Diagnosis Engine for Hybrid Systems using Structural Model Decomposition


 
 
 Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, and embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) architecture offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. How- ever, HyDE faces some problems regarding performance in terms of time and space complexity. This paper focuses on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic benchmark problem, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
 
 

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.

Bond graph modelling and simulation of fault scenarios in switched power electronic systems

A bond graph representation of switching devices known for a long time has been a modulated transformer with a modulus [Formula: see text] in conjunction with a resistor [Formula: see text] accounting for the ON-resistance of a switch considered non-ideal. Besides other representations, this simple model has been used in bond graphs for simulation of the dynamic behaviour of hybrid systems. A previous article of the author has proposed to use the transformer–resistor pair in bond graphs for fault diagnosis in hybrid systems. Advantages are a unique bond graph for all system modes, the application of the unmodified standard Sequential Causality Assignment Procedure, fixed computational causalities and the derivation of analytical redundancy relations incorporating ‘Boolean’ transformer moduli so that they hold for all system modes. Switches temporarily connect and disconnect model parts. As a result, some independent storage elements may temporarily become dependent, so that the number of state variables is not time-invariant. This article addresses this problem in the context of modelling and simulation of fault scenarios in hybrid systems. In order to keep time-invariant preferred integral causality at storage ports, residual sinks previously introduced by the author are used. When two storage elements become dependent at a switching time instance [Formula: see text], a residual sink is activated. It enforces that the outputs of two dependent storage elements become immediately equal by imposing the conjugate3 power variable of appropriate value on their inputs. The approach is illustrated by the bond graph modelling and simulation of some fault scenarios in a standard three-phase switched power inverter supplying power into an RL-load in a delta configuration. A well-developed approach to model-based fault detection and isolation is to evaluate the residual of analytical redundancy relations. In this article, analytical redundancy relation residuals have been computed numerically by coupling a bond graph of the faulty system to one of the non-faulty systems by means of residual sinks. The presented approach is not confined to power electronic systems but can be used for hybrid systems in other domains as well. In further work, the RL-load may be replaced by a bond graph model of an alternating current motor in order to study the effect of switch failures in the power inverter on to the dynamic behaviour of the motor.

Redundancy Relations for Fault Diagnosis in Hybrid Systems

The problem of fault diagnosis in hybrid systems is considered within the scope of analytical redundancy concept. Solution of the problem assumes the checking redundancy relations existing among measured system inputs and outputs. The method is proposed for redundancy relations design. This method involves appropriate transformations of the operating and operated subsystems and, then, conversion of the transformed operated subsystem given in strict feedback form to input-output representation. Pair algebra of partitions and differential geometric tools are involved for transformation of the operating and operated subsystems respectively.

Fault Diagnosis of Hybrid Systems: an Onboard Camera Model

In this paper we apply a method for diagnosing faults on hybrid systems to a model of a camera mounted on a mobile robot. A hybrid system is a system mixing continuous and discrete behaviors that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and try do detect faults exploiting a Fault Diagnosis Game on them, with two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behavior of the system and announces whether a fault has occurred or not. The case study we introduce here is a simplified model of a camera mounted on a mobile rover that has to take pictures of some defined locations of the environment. We add the possibility of a stuck fault on the camera motor to apply the theory and show its effectiveness.

A Geometric Method for Generating Discrete Trace Transition System of a Polyhedral Invariant Hybrid Automaton

Supervisory control and fault diagnosis of hybrid systems need to have complete information about the discrete states transitions of the underling system. From this point of view, the hybrid system should be abstracted to a Discrete Trace Transition System (DTTS) and represented by a discrete mode transition graph. In this paper an effective method is proposed for generating discrete mode transition graph of a hybrid system. This method can be used for a general class of industrial hybrid plants which are defined by Polyhedral Invariant Hybrid Automata (PIHA). In these automata there are no resetting maps, while invariant sets are defined by linear inequalities. Therefore, based on the continuity property of the state trajectories in a PIHA, the problem is reduced to finding possible transitions between all two adjacent discrete modes. In the presented method, the possibility and the direction of such transitions are detected only by computing the angle between the vector field and the normal vector of the switching surfaces. Thus, unlike the most other reachability methods, there is no need to solve differential equations and to do mapping computations. In addition, the proposed method, with some modifications can be applied for extracting Stochastic or Timed Discrete Trace Transition Systems.

A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.

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 system's 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.

An event-based approach to integrated parametric and discrete fault diagnosis in hybrid systems

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. These systems often exhibit hybrid behaviours, therefore, model-based diagnosis methods have to be based on hybrid system models. Most previous work in hybrid systems diagnosis has focused either on parametric or discrete faults. In this paper, we develop an integrated approach for hybrid diagnosis of parametric and discrete faults by incorporating the effects of both types of faults into our event-based qualitative fault signature framework. The framework allows for systematic design of event-based diagnosers that facilitate diagnosability analysis. Experimental results from a case study performed on an electrical power distribution system demonstrate the effectiveness of the approach.

Model-based fault diagnosis for hybrid systems: Application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So, the research ways aim at developing new and more powerful techniques for the detection of process fault. This article presents a method for the fault detection based on the comparison between the reference model evolution and the real system generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering.

Contribution to the Fault Tolerance for Hybrid Control Systems with Multiple Faults

Many diagnosis approaches are based in the assumption of single faults. This assumption may result to erroneous diagnosis statement in case where multiple faults occurs. Thereby multiple fault diagnosis is a challenging task especially in the control of large scale complex systems that can be viewed as hybrid systems. This owed to the fact that multiple faults are hard to detect because there consequences can mask or compensate to each other. The goal is to detect multiple faults as early as possible and provide a timely warning. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. This can be achieved by fault tolerant witch means that despites the faults occurrences the system is able to recover its original task with the same or degraded performance. Fault tolerance can be considered that it is constituted by two basic tasks, fault diagnosis and control redesign. In our work we addressed mainly to the first task introducing a method for multiple fault diagnosis in hybrid systems, while we propose a framework for fault tolerant hybrid control systems, which allows retaining acceptable performance under systems faults. The method is tested via a simple application to an electric power transmission system presented in our previous work.

Monitoring ability analysis and qualitative fault diagnosis using hybrid bond graph

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 system's fault monitoring ability. Additionally, we proposed a quantitative FDI framework for effective fault diagnosis for hybrid systems.

Monitoring and Fault Diagnosis of Hybrid Systems

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

Review Paper: Efficient numerical methods for the solution of stiff initial-value problems and differential algebraic equations

In recent years, after a prolonged period of intense activity, the study of numerical methods for solving stiff initial-value problems for ordinary differential equations and differential algebraic equations has reached a certain maturity. There now exist some excellent codes which are both efficient and reliable for solving these particular classes of problems. In this paper, we sketch some of the main theory which underpins stiff integration methods and we use this to describe, and put into context, some of the best codes currently available. By referencing only codes which have been thoroughly tested and are widely available, our aim is to direct users of numerical software to those codes which they should try initially if faced with the problem of solving ordinary differential equations of this type. An additional feature is that the codes which we propose serve as benchmarks against which any new codes can be evaluated.