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Model-Based Process Supervision: A Bond Graph Approach
Abstract
Model-based fault detection and isolation requires a mathematical model of the system behaviour. Modelling is important and can be difficult because of the complexity of the monitored system and its control architecture. The authors use bond-graph modelling, a unified multi-energy domain modelling method, to build dynamic models of process engineering systems by composing hierarchically arranged sub-models of various commonly encountered process engineering devices. The structural and causal properties of bond-graph models are exploited for supervisory systems design.
The structural properties of a system, necessary for process control, are elegantly derived from bond-graph models by following the simple algorithms presented here. Additionally, structural analysis of the model, augmented with available instrumentation, indicates directly whether it is possible to detect and/or isolate faults in some specific sub-space of the process. Such analysis aids in the design and resource optimization of new supervision platforms.
Static and dynamic constraints, which link the time evolution of the known variables under normal operation, are evaluated in real time to determine faults in the system. Various decision or post-processing steps integral to the supervisory environment are discussed in this monograph; they are required to extract meaningful data from process state knowledge because of unavoidable process uncertainties. Process state knowledge has been further used to take active and passive fault accommodation measures.
Several applications to academic and small-scale-industrial processes are interwoven throughout. Finally, an application concerning development of a supervision platform for an industrial plant is presented with experimental validation.
Model-based Process Supervision provides control engineers and workers in industrial and academic research establishments interested in process engineering with a means to build up a practical and functional supervisory control environment and to use sophisticated models to get the best use out of their process data.
Chapters (11)
Modern process engineering plants use complex equipment, control laws, and myriads of operation sequences and instrumentation. In complex and safety critical systems, such as chemical plants, nuclear power plants and airplanes, total failure of a component can be extremely hazardous; the Chernobyl and Bhopal tragedies are indicators of the extent of such damage. Even partial failures or malfunctions of process components or instrumentation increase the operating costs of the plant. Gross failures, such as accidents, are definitely more serious. These failures could be due to faulty design, faulty operation and human errors, to which sabotage and acts of terrorism may be added these days.
Because of the multidisciplinary nature of most industrial processes (mechanical, thermal, electrical,...), a unified modeling method is needed for analysis and model synthesis. The bond graph tool is well suited for this purpose. This methodology allows integrated modeling (independent of the physical nature of the studied system), precisely due to its graphical nature of display of the power exchange in a system, which include storage, dissipation and transformation [218]. Furthermore, the bond graph modeling methodology allows for the generation of not only a behavioral model [18, 27,217], but also it can be used for structural and causal analysis which are essential to design control and monitoring systems [81, 85, 86, 122, 262]. A bond graph model can be refined by graphically adding more elements like thermal losses or inertia and storage effects, without having to start all over again. More significantly, from the simulation point of view, the causal properties of the bond graph language enable the modeler to resolve the algorithmic level of modeling (e.g. singularity, invertibility, etc.) in the formulation stage, even before the detailed equations have been derived. Furthermore, the structural and causal properties provided by this graphical representation can be used (as developed in this book) for design of supervision systems. Therefore, bond graph modeling may be considered as an integrated computer aided design tool in the field of system engineering.
A control system refers to a set of devices used to manipulate the output of a system by altering its inputs, called commands. There are two types of control systems: manual and automatic. We will devote our discussion to the latter in this chapter.
Most of the early approaches for fault diagnosis and isolation were rule based. Such approaches use simple prediction rules to provide possible faults in a system and their causes. These methods suffer from incompleteness and inflexibility. Recent fault diagnosis methods are based on analysis of the underlying model structures and behavior of a system. Models serve as knowledge representation of a large amount of structural, functional and behavioral information and their relationship [8, 16, 50, 93, 139, 197, 198, 237, 239]. This knowledge representation is used to create complex cause-effect reasoning leading to construction of powerful and robust automatic diagnosis and isolation systems [2, 76, 98, 99, 114, 115, 261, 267].
Recent methods of fault diagnosis are based on the analysis of the underlying structure and behavior of a system [240]. Bond graph models serve as knowledge representation of a large amount of structural, functional and behavioral information and their relationship. This knowledge representation is used to create complex cause-effect reasoning leading to construction of powerful and robust automatic diagnosis and isolation systems. We have discussed a few of such qualitative reasoning methods in the previous chapter and reasoned that, in most cases, proper fault isolation is impossible with simple qualitative analysis. Full or partial quantitative analysis helps in better fault isolation. We introduce a few of the model based quantitative FDI methods in this chapter.
The methodology developed in the previous chapters is applied in this chapter to supervise an industrial steam generator. The purpose of this application is to design a supervision platform which allows one to control and to monitor the process in normal situations as well as in the presence of faults and failures. On the outset, we generate a bond graph model of the process and validate it so that the model can be used for ARR generation and its online implementation.
The analytical redundancy relation (ARR) derivation method presented in Chapter 5 follows the causality inversion of detectors (putting them as sources) to derive a closed form ARR expression. In this chapter, we show that closed form expressions for ARRs cannot be derived for all kinds of processes with all kinds of instrumentation because it may not be possible in certain cases to eliminate unknown variables from the model through symbolic algebra. We develop a few substitutions in this chapter which results in a new model structure to directly evaluate the residuals. The modified method leads to the same set of residuals which can be obtained through classical means, if the equations are symbolically resolvable. It also generates only the structurally independent residuals, and thus, reduces the computation time. Furthermore, the developed method leads to a numerical residual generation scheme which can be applied to all situations, irrespective of whether the set of equations can be symbolically resolved, or not.
Fault Tolerant Control (FTC) is performed through fault accommodation and/or system reconfiguration. In fault accommodation, the objective is to control the system under actual constraints. In system reconfiguration, part of the actual faulty system is replaced by another one [282]. The objective of FTC is to prevent local faults developing into serious failures.
If the number of fault parameters is more than the number of residuals or residuals are unstructured then some faults cannot be isolated even under single-fault assumption. In this chapter, a second level decision procedure or fault hypothesis testing for fault isolation is developed. Both qualitative and quantitative trend analysis techniques are applied for fault disambiguation.
For unambiguous fault isolation, one needs to use a lot of sensors in the process such that structured residuals can be generated. When residuals are unstructured, one may follow estimation based FDI approaches wherein the temporal behavior of the process (from measurements) is used to estimate the process parameters and then these process parameters are compared to their nominal values for fault isolation [17, 113, 114, 236, 237]. Furthermore, even when one or more faults in process parameters are directly isolated, it is required to estimate the degree of those faults to take appropriate fault accommodation measures, called fault tolerant control.
Fault Tolerant Control (FTC) relates to recovery from fault such that the system is controlled under actual constraints without replacing part(s) of the faulty system. FTC approaches can be classified into two categories: passive approach (e.g. robust control) and active approach (e.g. adaptive control [112, 118]).
... For the first time, bond graph modeling (was introduced by Prof. H.M. Paynter) with the innovative thought of simulating systems to analyze energy conversion [13]. For continuous model systems, bond graph modeling is a compound of bonds and junctions [14]. ...
... A brushless generator comprises of two synchronous machines, a rectifier and a small permanent magnet generator (PMG), on the same shaft [13]. One of the synchronous machines is the main generator, and the other smaller lower rating machine, with its field winding on the stator, works as the brushless exciter. ...
This paper deals with a procedure based on bond graph modeling of the brushless synchronous generator (BLSG). A bond graph is a graphical approach to modeling dynamic systems, which include multiple energy domains. As sub-models of BLSG, we use bond graph modeling to design a three-phase diode rectifier and synchronous generator in this study.
... The method is discussed in depth in [58][59][60][61]. Bond graphs can also be used to generate algorithms to monitor operations in industries [62]. It provides a way of pictorial representation of the entire system consisting of multiple disciplines [63]. ...
Fused Filament Fabrication (FFF) 3D printers are widely used for rapid prototyping and industrial applications. This research attempts to construct a dynamic modelling of an FFF 3-D printer gantry (2-D) to reduce manufacturing defects from extruder carriage error. Physical examples of machine-related errors were analyzed, and a major source of the errors observed were due to machine vibration and compliance. A six-dimensional non-linear dynamic model of the printer gantry was developed to explore the system vibrations; this model was derived using Newton-Euler method and considers the effects of damping, belt pre-load, and ramp up from input motor torque. The Lagrangian dynamic model was derived to get additional insight on energy transfer aspects and conservative Newton-Euler model validation. Using these models, a state-space model of the full system was developed for positioning and control. Examples of predictive control systems are demonstrated with their implementation in state-space control model. With appropriate value of design parameters, the passive control co-designs are expected to reduce machine related and vibrational errors in the prints without affecting the print speed. Detailed experimental case-studies of an example printer were performed by varying its speed, acceleration, jerk, and print height. A high-speed camera was used to record the gantry positions for each variation run. MATLAB vision was used to measure the position and velocity of the gantry. Most physical characteristics of the printer were either measured or analyzed (in Ansys) to present a realistic model to implement the dynamic model developed. The three models developed, i.e. ideal input model, dynamic model and the physical model from the experiments were compared to validate the dynamic model and to measure the errors in each. This work also presents a systematic development of a bond graph model of fused filament fabrication (FFF) 3D printer gantry. The iii model was checked for correctness and causality using the 20-SIM software. This model was further validated using the dynamic model created in MATLAB Simulink. The bond graph model gives important information about the flow of energy to each component of a dynamic system and is especially useful for complex non-linear mechanical systems. iv ACKNOWLEDGEMENTS
... The method is discussed in depth in [1][2][3][4]. Bond graphs can also be used to generate algorithms to monitor operations in industries [5]. It provides a way of pictorial representation of the entire system consisting of multiple disciplines [6]. ...
Energy flow (bond graph) modelling gives important information about the flow of energy to each component of a dynamic system and is especially useful for complex non-linear mechanical systems. This work presents a systematic development of a bond graph model of fused filament fabrication (FFF) 3D printer gantry. The model incorporates structural and belt stiffness, damping and input torque. The model was checked for correctness and causality using the 20-SIM software. The model was further validated using MATLAB-Simulink using parameters obtained for an example printer characterized in a lab environment. The bond graph model gives a unique view into modelling of the extruder carriage dynamics in FFF and can be applied to specific problems. It will also give interesting information on the controllability and system integration of the printer hardware.
In this article, a special type of fault isolation problem is reported where temporal information is required for the estimation of parameters from susceptible fault sub-space. Any physical system always obeys certain mathematical constraints under the normal operating condition, and that is incorporated in a behavioral model, which is essentially flowing and force or moment balance. In a fault detection and identification (FDI) model, only concurrent states are known. In FDI, the constraint relationships need to be derived only in terms of known variables, i.e., the measurements and nominal parameters, which are termed analytical redundancy relations (ARRs). The numerical evaluation of ARRs is residuals, and those oscillate within a definite bound of error when computed using test data. In this work, a novel function is formulated using the ARRs at different time instants and that function is minimized to estimate the suspected parameter values belonging to the non-isolable sub-space. Single-fault hypothesis is considered, and a genetic algorithm (GA) is used for the optimization. This FDI analysis may be beneficial for power hydraulic-driven heavy machinery such as hydraulic excavators, dumpers, and front-end loaders, which are mostly used in mines and construction sites.
In modern technological systems, the primary concern is performance, safety, and dependability. As a result, a reliable supervision platform that includes fault detection and isolation is useful to avoid malfunctions or failures. As a result, fault diagnosis is very helpful to the hydraulic system’s safety and dependability. This chapter discusses a unified method for bond graph model-based quantitative Fault Detection and Isolation. Several Fault Detection and Isolation (FDI) techniques all universally accept the Bond graph model [1–7].
In this paper, a dynamic observation window based prognosis method is proposed for the electric scooter in the presence of intermittent faults and imperfect maintenance. Firstly, analytical redundancy relations derived from the nonlinear bond graph model and fault signature matrix are employed for fault detection and isolation, and an adaptive fruit fly optimization algorithm is developed to estimate the magnitude, appearing and disappearing instants of intermittent faults. Secondly, the degradation characteristics of the intermittent fault are extracted using the concept of dynamic observation window. After that, a joint degradation model is established, where three impact factors are introduced to quantify the impacts of imperfect maintenance on intermittently faulty component. Based on this model, the remaining useful life of the component subjected to intermittent fault and imperfect maintenance can be predicted. Finally, the effectiveness of the proposed method is experimentally verified.
This presentations gives an overview of reconfigurable vehicles with redundancy, vehicle dynamics modelling and simulation, braking systems, fault diagnosis, fault tolerant navigation and so on.
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