Refine
H-BRS Bibliography
- yes (33)
Departments, institutes and facilities
Document Type
- Article (16)
- Conference Object (12)
- Book (monograph, edited volume) (3)
- Part of a Book (2)
Year of publication
Language
- English (33)
Keywords
This book presents bond graph model-based fault detection with a focus on hybrid system models. The book addresses model design, simulation, control and model-based fault diagnosis of multidisciplinary engineering systems. The text beings with a brief survey of the state-of-the-art, then focuses on hybrid systems. The author then uses different bond graph approaches throughout the text and provides case studies.
In this paper, residual sinks are used in bond graph model-based quantitative fault detection for the coupling of a model of a faultless process engineering system to a bond graph model of the faulty system. By this way, integral causality can be used as the preferred computational causality in both models. There is no need for numerical differentiation. Furthermore, unknown variables do not need to be eliminated from power continuity equations in order to obtain analytical redundancy relations (ARRs) in symbolic form. Residuals indicating faults are computed numerically as components of a descriptor vector of a differential algebraic equation system derived from the coupled bond graphs. The presented bond graph approach especially aims at models with non-linearities that make it cumbersome or even impossible to derive ARRs from model equations by elimination of unknown variables. For illustration, the approach is applied to a non-controlled as well as to a controlled hydraulic two-tank system. Finally, it is shown that not only the numerical computation of residuals but also the simultaneous numerical computation of their sensitivities with respect to a parameter can be supported by bond graph modelling.
Bond Graph Methodology
(2010)
Nowadays, engineering systems are becoming increasingly complex and, for design purposes, must be considered as multidisciplinary systems made up of components from different engineering disciplines. With regard to the systematic development and the analysis of models, interdisciplinary methodologies supported by software become more and more important. Bond graphs are a graphical description formalism particularly suited for multidisciplinary systems and used by modellers across the world.
Bond Graph Methodology gives a comprehensive, in-depth representation of the state of the art, including recent results gathered from research articles, dissertations and contributions by the author on a number of topics. The structured and rigorous presentation systematically covers model development, analysis of models, numerical computation of models and modern software that can be used for a bond graph approach. The book also includes a range of case studies illustrating various applications of the methodology and provides a glossary.
Bond Graph Methodology addresses fundamentals, as well as advanced topics, e.g., models of variable structure, bond graphs for sensitivity analysis and generation of equations for the study of robustness. The compilation and presentation of the material has been inspired by the author's extensive experience in research and teaching.
A useful text for advanced courses in modelling, simulation and control, Bond Graph Methodology can also be used for self-study. It has been designed to serve readers interested in the subject of bond graph modelling and those with expertise in related areas, as well as members of the worldwide community of bond graph modellers.
BGML - a novel XML format for the exchange and the reuse of bond graph models of engineering systems
(2006)
This paper picks up on one of the ways reported in the literature to represent hybrid models of engineering systems by bond graphs with static causalities. The representation of a switching device by means of a modulated transformer (MTF) controlled by a Boolean variable in conjunction with a resistor has been used so far to build a model for simulation. In this paper, it is shown that it can also constitute an approach to bond graph based quantitative fault detection and isolation in hybrid system models. Advantages are that Analytical Redundancy Relations (ARRs) do not need to be derived again after a switch state has changed. ARRs obtained from the bond graph are valid for all system modes. Furthermore, no adaption of the standard sequential causality assignment procedure (SCAP) with respect to fault detection and isolation (FDI) is needed.
This paper proposes a novel approach to the generation of state equations from a bond graph (BG) of a mode switching linear time invariant model. Fast state transitions are modelled by ideal or non-ideal switches. Fixed causalities are assigned following the Standard Causality Assignment Procedure such that the number of storage elements in integral causality is maximised. A system of differential and algebraic equations (DAEs) is derived from the BG that holds for all system modes. It is distinguished between storage elements with mode independent causality and those that change causality due to switch state changes.
Failure prognostic builds up on constant data acquisition and processing and fault diagnosis and is an essential part of predictive maintenance of smart manufacturing systems enabling condition based maintenance, optimised use of plant equipment, improved uptime and yield and to prevent safety problems. Given known control inputs into a plant and real sensor outputs or simulated measurements, the model-based part of the proposed hybrid method provides numerical values of unknown parameter degradation functions at sampling time points by the evaluation of equations that have been derived offline from a bicausal diagnostic bond graph. These numerical values are computed concurrently to the constant monitoring of a system and are stored in a buffer of fixed length. The data-driven part of the method provides a sequence of remaining useful life estimates by repeated projection of the parameter degradation into the future based on the use of values in a sliding time window. Existing software can be used to determine the best fitting function and can account for its random parameters. The continuous parameter estimation and their projection into the future can be performed in parallel for multiple isolated simultaneous parametric faults on a multicore, multiprocessor computer.
The proposed hybrid bond graph model-based, data-driven method is verified by an offline simulation case study of a typical power electronic circuit. It can be used to implement embedded systems that enable cooperating machines in smart manufacturing to perform prognostic themselves.