Sanjoy K. Ghoshal

Bicausal bond graphs for supervision : From fault detection and isolation to fault accommodation

Model-based fault detection and isolation (FDI) requires an analytical system model from which fault indicators can be derived by assigning proper computational causalities. Many bond graph (BG) model-based techniques for FDI have been developed in recent past. Furthermore, many other advances have been made in the field of control engineering applications of BG modelling. Supervision systems not only perform FDI, but also take the necessary steps for fault accommodation. Fault accommodation is done either through system reconfiguration or through fault tolerant control (FTC). In this paper, it is shown that bicausal BG modelling proves to be a unified approach for sensor placement from the FDI and FTC viewpoint, identification of hardware redundancies for system reconfiguration, generation of fault indicators, estimation of fault parameters for fault accommodation, inversion of systems and actuator sizing for FTC, etc. It is shown that the use of bicausalled BG helps to integrate many of the recently developed advances made in the field of control engineering into development of complex supervision systems.

Improvements to Single-Fault Isolation Using Estimated Parameters

A method for finer fault isolation or localization in the model-based fault detection and isolation (FDI) paradigm is developed using parallely computed bond graph models. Many of the existing modelbased FDI methods are based on the evaluation of model consistency expressed in terms of analytical redundancy relations (ARR). These evaluations lead to residuals, and a number of sensors are to be installed in the plant to generate independent signatures needed for fault isolation. However, all the possible faults may not be isolable with the available instrumentation, and it is sometimes expensive or technically impossible to install necessary sensors in the plant to physically measure each and every state. In such situations, all component faults may not be uniquely isolated. However, a unique fault parameter subspace can be identified. One of the possible solutions, as proposed in this article, is to estimate parameters of that subspace from the ARR by assuming a single-fault hypothesis and then to incorporate the estimated values in separate models to run parallel with the plant during the fault. Thereafter, comparison of model behaviors leads to localization of the faulty parameters. This method is applied to an example system.

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.

Model based fault diagnosis, fault tolerant control and reconfiguration of hydraulic and thermo-fluid processes using analytical redundancy

Analytical redundancy relation (ARR) based approach for fault detection and isolation (FDI) is presented in this article with applications to a hydraulic and a thermo-fluid process. Bond graph modelling, which allows unified representation of multi-energy domain system dynamics, is used to develop model based quantitative FDI schemes. Also, some issues of fault tolerant control and system reconfiguration have been taken up with model simulation. It is shown that ARRs can be systematically derived by exploring causality in bond graph models and fault indicators can be interactively designed. The sensitivity of fault indicators has been validated through simulation.

Multiple fault disambiguations through parameter estimation: a bond graph model-based approach

To ensure safe operation of industrial processes, automated Fault Detection and Isolation (FDI) procedures are implemented in their supervision platforms. In the safety-critical and environmentally hazardous processes, it is impossible to introduce all kinds of faults and then to derive their consequences. Qualitative determination of consequences of different faults can be misleading in complex dynamical systems. Therefore, simulation of a prototype model turns out to be a practical and an economical solution for the development of a complete Knowledge-Base (KB). Consequently, the intelligence acquired by KB from the simulated models is used to fine-tune the Decision Support System (DSS) such that false alarms and misdetections are minimised. A method for model-based multiple FDI by using Analytical Redundancy Relations (ARRs) and parameter estimation is developed in this paper. Parameter estimation is an essential prerequisite for fault accommodation through system reconfiguration or Fault Tolerant Control (FTC). Bond graph modelling is used to describe the process models and then the model is used to derive the ARRs and fault candidates. Parameter values corresponding to the fault-subspace are estimated by minimising a function of the ARRs. Modelling uncertainties arising out of parameter estimation and sensor noise are taken care by using a passive approach for robust FDI. The developed technique is applied to monitor an open-loop non-linear thermo-fluid process.

Reconfiguration of an industrial steam generator using bond graph modelling

In this paper, we survey some recent advances made in the field of Bond Graph (BG) modelling and show that it is well suited to solve modelling, fault diagnosis and Fault Tolerant Control (FTC) problems in complex processes. We consider the reconfiguration scheme for a steam generator process, which is a safety-critical complex thermo-fluid process involving storage and transport of under saturated and saturated fluids, as well as phase transformations. BG modelling, which is a unified tool for multienergy domain system representation, is not only used to model the process but also to generate the fault indicators and to determine various redundancies. These redundancies are then used to determine possible system reconfigurations and Operating Modes (OMs), by taking various operating constraints (equipment availability, saturations, power ratings, etc.) into consideration. The developed algorithms are implemented to supervise the steam generator process and the experimental results are presented.

Dynamic analysis of a closed-circuit hydrostatic summation drive using bent axis motors

This article investigates the dynamic performance of a closed-circuit hydrostatic summation drive that has been carried out through system modeling and simulation. The proposed drive consists of a variable displacement piston pump and bent axis motors. The performances of the hydrostatic drive have been studied for its two different modes of operations, using single motor and two motors. The changing over of the drive mode is accomplished by on-/off-controlled direction control valve. Bondgraph simulation technique is used to model the hydrostatic drives. The characteristics of the loss coefficients of the drive are obtained from the steady-state model and they are identified through experimental investigation. Using them, the overall dynamic model of the hydrostatic drive has been validated experimentally. With respect to some critical parameters, the performances of the hydrostatic drive have also been studied.

Multiple Fault Isolation to a Servo-Valve Controlled Motor Transmission System

Abstract Model based quantitative fault diagnosis procedure is based on generation of fault indicators, structural analysis and finally estimation of parameters. In this article, bond graph modelling is used to describe the system model and generate fault indicators by evaluating a set of Analytical Redundancy Relations (ARR), which are obtained from differentially causalled bond graph model to satisfy inverse system dynamics. Multiple fault case is realised by changing the parameters simultaneously. Thereafter the parameters related only to unstructured part of the fault subspace are estimated by optimising an objective function containing ARRs. Sometimes the derivative form of ARRs creates problem of singularity in estimation, and to avoid such a singularity problem we have estimated the corresponding parameters directly from constraint relations and thereafter those values are called in the main objective function to estimate rest of the parameters. The algorithm provides quicker fault isolation because it does not need several model simulations; thereby making it suitable for real-time process supervision.

Bond graph model based design of supervision algorithm for distributed fault tolerant control systems

For distributed analytical model based fault detection, some additional signals besides those required for distributed process control are required to evaluate residuals. This paper presents a graphical means of identifying those signals for design of a supervision system by analysing the causal structure in the bond graph model of a process. The global fault isolation scheme is initially implemented at a central monitoring system based on the alarm states generated at distributed controlling units. In safety critical systems, to minimise dependence on network communication, causality analysis is used to identify appropriate sensors that lead to information decoupling between various localised units of the process. Consequently, each controlling unit, called a smart station, is able to perform its own fault isolation and Fault Tolerant Control (FTC). The central monitoring system is used only for supervision of interfaces between different smart stations and operator mode management.

Comparison of the efficiency of the high speed low torque hydrostatic drives using bent axis motor: An experimental study

This article is aimed at analysing the steady-state performance of four hydrostatic drives and compares their overall efficiency. The speed of the hydrostatic drives is controlled by speed controlled vane pump, variable displacement flow compensated pump, variable displacement pressure compensated pump and proportional direction controlled valve. Bondgraph simulation technique is used to model the hydrostatic drive. The relationships of the loss coefficients with the state variables obtained from the model are identified through experimental investigation. Using them, at different torque levels, the performances of the hydrostatic drives are studied on their slips, torque losses and the overall efficiencies and they are validated experimentally. It is found that hydrostatic drive using speed controlled vane pump exhibits the maximum efficiency, whereas the poorest efficiency is shown by the valve controlled system out of the four drives considered in the analysis.