Browsing by Subject "Bond graph"
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Item Design and implementation of a software framework to model and simulate engineering systems using bondgraphs(2015-12) Leal Vasquez, Marihebert Josefina; Barber, Suzanne; Fernandez, Benito R.This report presents the development of a software framework for deriving explicit state equations in symbolic form of physical systems described by bond graphs. This program called Bond Graph Tool is an open-source object oriented implementation in Python, using the Tkinker and SymPy libraries. The Tkinker library has several functions that enables the user to command operations and display the results. SymPy is a Python library for symbolic mathematics, which permits the state-equations derived from the Bond Graphs in symbolic form. The Bond Graph Tool provides a graphic interface for drawing andediting Bond Graphs. The program allows to automatically assign the causalities on the Bond Graph. Output from the program is in the form of symbolic equations. The program handles the basic 1-port and 2-port elements as well as multiple ports junctions and derivative causality. The current version of the program, however, has limitations in handling several di cult features in bond graph.Item Fault detection and model-based diagnostics in nonlinear dynamic systems(2010-12) Nakhaeinejad, Mohsen; Bryant, Michael D.; Driga, Mircea D.; Fahrenthold, Eric P.; Fernandez, Benito; Longoria, Raul G.Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied. A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox. A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable.Item Modeling, control, fault detection and isolation of chemical processes using a bond graph framework(Texas Tech University, 2009-05) Zhang, XiA multitude of different approaches have been proposed to cope with external disturbances and unpredictable faults that are associated with chemical processes. A large number of these approaches rely on a model to deduce and predict the process performance. In many cases, the model neither represents the distinct physical domains (e.g., as reaction, hydraulic, mechanic, electric, etc.) nor their inter-dependencies and therefore is unable to predict critical information such as root cause of a fault. In this work, a unified modeling concept, the bond graph is used to model multiple domains. The basic variable of a bond graph is power that unifies the distinct domains. Additionally, the bond graph network reflects the physical structure in which power exchanges are tracked and the dynamics associated with power conversions can be captured quantitatively. Thus, a complete model of the process can be developed. Bond graph theory embodies causality - the cause and effect relationship between variables. This feature along with power conservation will be emphasized in this work to facilitate causal control design and fault detection and isolation. The methodology begins with extending bond graph theory into the realm of biochemical reactions, so that a unified modeling platform is obtained for biochemical processes involving biochemical reactions, hydraulics, and mechanics. Then a biochemical process for the purpose of wastewater treatment is used as the testbed to validate the extension. And with the completed model of the process, concepts on control design and fault detection and isolation under the unified bond graph framework are proposed and demonstrated using several examples.