Hybrid Bond Graph Model Research Articles

Discrete Component Prognosis for Hybrid Systems Under Intermittent Faults

Prognosis of discrete component with intermittent fault in hybrid systems is challenging since the component has only two states (i.e., ON and OFF) and no associated physical parameter in the model can quantify the degradation. This article aims to solve the discrete component prognosis problem under the model-based paradigm. First, the fault detection and isolation module help find the possible faulty discrete components. Based on the isolated possible faulty discrete components, Levy flight biogeography-based optimization is proposed to identify the faulty discrete component states, as well as the fault appearing and fault disappearing instants. Second, a Weibull function-based degradation model which can capture the duration evolution of intermittent fault of discrete component in observation window (OW) is developed using coordinate reconstruction approach, and the degradation model coefficients can be calculated from the fault identification results. After that, the concept of failure threshold for faulty discrete component is defined based on the ratio of fault duration to OW, which enables the prognosis of intermittent fault in discrete component. Finally, the proposed methodologies are validated by experiment results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the intermittent fault prognosis problem of discrete components (e.g., relays and hydraulic valves) in hybrid systems. Existing fault prognosis researches do not consider discrete component which is an important part of hybrid systems. For the intermittent fault prognosis of discrete component, the observation window (OW) concept and coordinate reconstruction (CR) method are proposed to establish the degradation model, and the ratio of fault duration to OW is used to define the failure threshold of discrete component. To show the effectiveness of the proposed methods, an application on a hybrid circuit system is considered. It is noted that the degradation pattern (e.g., increase of frequency or duration of intermittent fault) of discrete components may vary in different systems, while the degradation process can be quantified by the OW and CR methods developed in this article, which enables the prognosis of intermittent fault in discrete component for various hybrid industrial systems. The proposed approach can be applied to industrial hybrid systems if the following conditions are satisfied: 1) the hybrid bond graph model of the monitored system can be established, based on which the fault detection and isolation can be implemented and 2) the monitored system contains multiple discrete components suffering from intermittent faults whose appearing and disappearing instants can be identified by certain method.

The Research of Hybrid Bond Graph Modeling

Hybrid powertrain is a form of energy coupling. Involve two or more forms of energy. Using bond graph analysis the vehicle and engine, motor, batteries and other components. Using bond graph analysis the vehicle and the components of engine, motor, batteries, etc. Research the bond graph modeling of the return flow stepless variable speed drive system of hybrid electric vehicle drive system convenient for the simulation analysis.

Real time algorithm based on time series data abstraction and hybrid bond graph model for diagnosis of switched system

In this paper, we propose a real time algorithm to realize a diagnosis of switched systems for abrupt parametric faults. This algorithm is based on interaction between a Qualitative Diagnosis (QD) and a monitoring component that performs a Qualitative Trend Analysis (QTA) of residual signals generated from Bond Graph (BG) elements called residual sinks. The QTA is applied in order to on-line detect change in the mean of residual signal based on combination of Piecewise Aggregate Approximation (PAA) with Page-Hinkley Test (PHT). The QD procedure is performed in two stages. In the first off-line stage, Symbolic Fault Signature Matrix (SFSM) is generated from a Parameterized Temporal Causal Graph (PTCG). The PTCG is valid for all system modes and deduced from a unified Hybrid Bond Graph (HBG) model by converting its elements into node and labeled edge. Each entry in the SFSM matrix gives the residual symbolic signature which corresponds to the lower-order signature predicted using the PTCG model by propagating initial deviation from the fault parameter in the label of edge to the residual node. In the second on-line stage, trend extraction by linear regression is triggered after change detection in order to estimate the lower order time-derivative symbol for each residual sinks. Subsequently, we propose a stepwise similarity measure for fault isolation task. The functioning of this approach is illustrated in simulating with a switched quarter-car active suspension system.

Multiple parametric faults diagnosis for power electronic circuits based on hybrid bond graph and genetic algorithm

Abstract Aiming at the problem to diagnosis multiple parametric faults in power electronic circuits, a new diagnosis framework based on hybrid bond graph (HBG) and genetic algorithm (GA) is presented. Firstly, the circuits are modeled by HBG modeling technique, in which the equivalent models of the key components are considered. Then, a set of residuals equations and a fault signature matrix (FSM) are derived from the HBG model. Each faulty component exhibits their degradation symptoms on residuals equations. Thus, faults can be detected by comparing residuals with fault detection thresholds and isolated based on FSM. Finally, GA method is employed to identify the component faults. The fitness function of GA is constructed by the residuals equations in which the faulty components are contained. Simulation and experiment are carried out to verify the feasibility and effectiveness. The results show that the developed method is applicable to diagnosis both single and multiple parametric faults.

The hybrid bond graphs modelling on anti lock braking system sliding mode

This paper presents application of the hybrid bond graphs modeling and simulation laboratory anti-lock braking model using Dymola. It is shown on this practical example that the use of Dymola software package will simplify the modeling and simulation. The results and simulation obtained by the hybrid bond graph modeling are presented in this paper.

Hybrid bond graph model based for robust fault detection and isolation

The article deals with the robust fault detection and isolation based on linear fractional transformation hybrid bond graph. The main objective is to improve the robustness of fault detection step in the presence of parametric uncertainties in hybrid models in order to minimize the false alarms. The scientific interest of the proposed method is the use of only one tool – the bond graph not only for dynamic modelling of uncertain hybrid system but also for generation of adaptive thresholds needed in residual evaluation step. For this task, hybrid bond graph uncertain model approach with controlled junctions in linear fractional transformation form (allowing to represent graphically the parametric uncertainties) is first proposed to represent all the modes of the hybrid system. Second, based on causal and structural properties of the hybrid bond graph, analytical redundancy relations (with nominal and uncertain part) valid for all the modes are then derived systematically from the diagnostic hybrid bond graph. An application to a hydraulic system is used to illustrate this method.

An Integrated Approach to Mode Tracking and Diagnosis of Hybrid Systems

In this paper, information from a hybrid bond graph (HBG) model and from discrete-event systems (DESs) is integrated into a fault diagnosis method for hybrid systems. In a pure-HBG framework, mode-change detection and isolation is handled by the mode-change signature and the mode-change signature matrix. In a DES approach, discrete states and faults are traced based on observable events (e.g., control signals) and diagnosers. Based on the two approaches, a new diagnoser that is driven by both observable events and consistency indicators generated by continuous residuals is presented. The proposed method allows not only to effectively trace the system mode but also to decide whether this mode is faulty or normal. The new method is presented and implemented on a testbed. The experimental results presented in this paper demonstrate the effectiveness of the method.

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.

Robust fault detection and isolation of hybrid systems with uncertain parameters

This article addresses a robust fault detection and isolation procedure which is applicable for a hybrid system undergoing discrete mode changes. Global analytical redundancy relations, which may undergo change in their structure depending on the switched mode, are derived from the diagnostic hybrid bond graph model of the system. For fault detection and isolation analysis, the residuals, evaluated at every instant from the global analytical redundancy relations, are tested against adaptive thresholds which are changed with mode shift and variation of states, but not changed due to any fault. The parametric uncertainties are included in the diagnostic hybrid bond graph through the linear fractional transformation approach and the adaptive thresholds are obtained from the modified diagnostic hybrid bond graph model. The residuals are made robust in a sense that these would always remain within the upper and lower threshold bound for any perturbation except fault occurrence. For application of the robust fault detection and isolation analysis, a hybrid thermo-fluid system is selected whose behavioural evolution combines both discrete as well as continuous changes of states. It is shown through simulation that with parameter uncertainties in a hybrid system subject to frequent mode shift, robust fault detection and isolation is successfully achieved using the passive approach. The novelty of this work lies in the integration of the model-based diagnosis principle for hybrid systems with the approach for robust residual threshold generation for systems having uncertain parameters.

Fault Diagnosis in Hybrid Systems using Possible Conflicts

Hybrid systems, whose behavior exhibit continuous and discrete event dynamics, are present in many industrial environments. The complexity of such behavior makes the online fault diagnosis task very challenging. In this work we propose an accurate and timely online fault diagnosis approach for hybrid systems. Our approach uses the Hybrid Bond Graph modeling methodology to avoid the precompilation of all possible modes in the system. Structural decomposition of Hybrid Bond Graph models, obtained by means of Possible Conflicts, together with its implementation using Block Diagrams, allows the diagnosis system be quickly reconfigured when a mode change occurs. Moreover, this diagnosis approach has the ability to track the behavior of the system during the transitory states caused by mode changes. Diagnosis results using a four-tank system demonstrate the capabilities of the proposal.

Multiple Faults Isolation for Hybrid Systems with Unknown Fault Pattern

This work is concerned with multiple faults isolation for hybrid systems based on Global Analytical Redundancy Relationships (GARRs) approach. GARRs are derived from the Hybrid Bond Graph (HBG) model of a hybrid system with a specified causality assignment procedure. In this article, multiple faults are considered in a complex hybrid system and these faults can develop during a mode when the faults are not detectable. Once a fault is detected, a fault candidates set is generated from mode dependent-fault signature matrix (MD-FSM) tables and a set of fault pattern hypothesis is created from the fault candidates set for further refinement. Fault isolation is carried out using a multiple nonlinear least square optimization (MNLSO) algorithm. The developed technique can deal with multiple faults with unknown pattern. The fault could be of incipient or abrupt nature. The simulation results show the effectiveness of the proposed method.

A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems

The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), which was deployed at the NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges. ADAPT contains a large number of interconnected components, and a set of circuit breakers and relays that enable a number of distinct power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems (such as motors), and fluid systems (such as pumps). The system components are susceptible to different types of faults, i.e., unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid Transcend, which is a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enable diagnostic analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of the methodology.

Comprehensive Diagnosis of Complex Electrical Power Distribution Systems

Electrical power distribution systems are composed of heterogeneous components, which include continuous power sources, discrete relays, passive and active loads, and fast-switching power conversion subsystems. This heterogeneity introduces significant challenges for model-based diagnosis, such as building accurate models, and generating fast and accurate diagnoses while ensuring robustness to measurement noise and modeling errors. In this paper, we present a comprehensive methodology for the diagnosis of parametric and discrete faults in electrical power distribution systems that include dc and ac components. We use a hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. Simulation and experimental results on a real-world electrical power distribution system demonstrate the effectiveness of our methodology.

HYBRSIM—a modelling and simulation environment for hybrid bond graphs

Bond graphs are a powerful formalism to model continuous dynamics of physical systems. Hybrid bond graphs introduce an ideal switching element, the controlled junction, to approximate continuous behaviour that is too complex for numerical analysis (e.g. because of non-linearities or steep gradients). HYBRSIM is a tool for hybrid bond graph modelling and simulation implemented in Java and is documented in this paper. It performs event detection and location based on a bisectional search, handles run-time causality changes, including derivative causality, performs physically consistent (re-)initialization and supports two types of event iteration because of dynamic coupling. It exports hybrid bond graph models in Java and C/C++ code that includes discontinuities as switched equations (i.e. pre-enumeration is not required).

Control of resistivity, microstructure, and stress in electron beam evaporated tungsten films : A. K. Sinhaet al. J. Vac. Sci. Technol. 10 (3), 436–444 (May-June 1973)

Electrical power distribution systems are composed of heterogeneous components, which include continuous power sources, discrete relays, passive and active loads, and fast-switching power conversion subsystems. This heterogeneity introduces significant challenges for model-based diagnosis, such as building accurate models, and generating fast and accurate diagnoses while ensuring robustness to measurement noise and modeling errors. In this paper, we present a comprehensive methodology for the diagnosis of parametric and discrete faults in electrical power distribution systems that include dc and ac components. We use a hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. Simulation and experimental results on a real-world electrical power distribution system demonstrate the effectiveness of our methodology.