Multiple Faults Isolation for Hybrid Systems with Unknown Fault Pattern

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.

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.

This paper deals with a model builder for Hybrid Dynamic Systems (HDS), where technological components including sensors in an iconic format can be automatically dragged from a database and interconnected to produce the overall HDS, following industrial technological schemes. Once the model has been created, its consistency can be checked, and a block diagram simulation can be performed. The theory behind the software is based on Hybrid Bond Graphs (HBG), whose structural and causal properties will be exploited for the automatic generation of the diagnosis algorithms, which will be implemented online. Compared with developed programs for automated modeling of HDS, the presented methodology is valid for continuous and hybrid systems, and the HBG is used not only for block diagram simulation builders but also for the generation of Fault Detection and Isolation algorithms. The theory is illustrated by an industrial application that consists of the pneumo-electrical control of metro rolling stock.

Nowadays, a wide number of manufacturing systems are usually coupled discrete and continuous dynamic behaviors. This paper deals with the design of a novel framework related to the fault diagnosis issue merging quantitative and qualitative reasoning so as to accurately monitor several fault kinds affecting such hybrid systems. Two different structural fault diagnosis approaches are compared. Firstly, the Hybrid Bond Graph (HBG) representation (quantitative way) is used to obtain the Global Analytical Redundancy Relations (GARRs) dedicated to Fault Detection and Isolation (FDI) tasks. Secondly, the qualitative approach utilizes the possible conflicts which are deduced from the Directed Behavioral Hypergraph (DBH) description and able to study the temporal and qualitative impacts related to sensor and actuator faults. Afterwards qualitative and quantitative methods are compared and discussed so as to analysis the ability to diagnose the dynamical hybrid systems.

On-line robust graphical diagnoser for hybrid dynamical systems

A fault detection and isolation methodology-theory and application

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.

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

Signed Bond Graph for multiple faults diagnosis

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

This paper investigates the issue of evolutionary design of open-ended plants for hybrid dynamical systems--i.e. both their topologies and parameters. Hybrid bond graphs are used to represent dynamical systems involving both continuous and discrete system dynamics. Genetic programming, with some special mechanisms incorporated, is used as a search tool to explore the open-ended design space of hybrid bond graphs. Combination of these two tools--i.e., hybrid bond graphs (HGBs) and genetic programming (GP)--leads to an approach called HBGGP that can automatically generate viable design candidates of hybrid dynamical systems that fulfill predefined design specifications. A comprehensive investigation of a case study of DC-DC converter design demonstrates the feasibility and effectiveness of the HBGGP approach. Important characteristics of the approach are also discussed, with some future research directions pointed out.

Improving Multiple Fault Diagnosability using Possible Conflicts

In this paper, we shall show that an unlimited number of additive single faults can be isolated under mild conditions if a general isolation scheme is applied. Multiple faults are also covered. The approach is algebraic and is based on a set representation of faults, where all faults within a set can occur simultaneously, whereas faults belonging to different fault sets appear disjoint in time. The proposed fault detection and isolation (FDI) scheme consists of three steps. A fault detection (FD) step is followed by a fault set isolation (FSI) step. Here the fault set is isolated wherein the faults have occurred. The last step is fault isolation (FI) of the faults occurring in a specific fault set, i.e. equivalent with the standard FI step.

The need for reliability and robustness in present day systems requires that they possess the capability for accommodating faults in the controlled plant. Fault accommodation requires tight integration of online fault detection,isolat ion, and identification with the system control loop. This paper develops an effective fault diagnosis scheme for plants that mainly exhibit continuous behavior,but include supervisory control making the overall dynamic behavior hybrid in nature. We use hybrid bond graph models to develop diagnosis algorithms that combine hybrid behavior tracking,m ode estimation, and qualitative-quantitative reasoning techniques. The effectiveness of the approach is demonstrated with example scenarios generated from a three-and five-tank systems with control valves and sources that can be turned on and o. by supervisory control.KeywordsHybrid SystemAutonomous TransitionBond GraphFault IsolationSupervisory ControllerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The paper focuses on single and multiple current sensors fault detection and isolation (FDI) for induction motor (IM) drives using only two current sensors. Two model based diagnosis methods are proposed to estimate the current residuals dynamics in the stationary reference frame: The first one is a differential algebraic estimation of fault dynamics and the second one is based on a combination of the Robust Integral Sign of the Error (RISE) observer with the algebraic approach. After detecting the fault occurrence, a logic based decision unit is then built to identify the faulty sensors. The resulting residuals are robust to load torque disturbances. Moreover, the developed fault estimators are simple with a 1-D model, based only on the input–output measurements and their derivatives and do not require a bank of observers. Furthermore, the used residual threshold is well defined and is suitable for the whole operating range. The ability of diagnosing the recovery of a sensor from a fault is also demonstrated. The validity of the developed approaches is analytically proved and a comparative study is carried out between the two FDI schemes.