471 research outputs found
A model-based reasoning architecture for system-level fault diagnosis
This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators.
The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.Ph.D.Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yora
Effects of park energy on spark plug fault recognition in a spark ignition engine
The increasing demands for fuel economy and emission reduction have led to the development of lean/diluted combustion strategies for modern Spark Ignition (SI) engines. The new generation of SI engines requires higher spark energy and a longer discharge duration to improve efficiency and reduce the backpressure. However, the increased spark energy gives negative impacts on the ignition system which results in deterioration of the spark plug. Therefore, a numerical model was used to estimate the spark energy of the ignition system based on the breakdown voltage. The trend of spark energy is then recognized by implementing the classification method. Significant features were identified from the Information Gain (IG) scoring of the statistical analysis
A framework for aerospace vehicle reasoning (FAVER)
Airliners spend over 9% of their total revenue in Maintenance, Repair, and Overhaul
(MRO) and working to bring down the cost and time involved. The prime focus is on
unexpected downtime and extended maintenance leading to delays in the flights, which
also reduces the trustworthiness of the airliners among the customers. One of the effective
solutions to address this issue is Condition based Maintenance (CBM), in which the
aircraft systems are monitored frequently, and maintenance plans are customized to suit
the health of these systems. Integrated Vehicle Health Management (IVHM) is a
capability enabling CBM by assessing the current condition of the aircraft at component/
Line Replaceable Unit/ system levels and providing diagnosis and remaining useful life
calculations required for CBM. However, there is a lack of focus on vehicle level health
monitoring in IVHM, which is vital to identify fault propagation between the systems,
owing to their part in the complicated troubleshooting process resulting in prolonged
maintenance. This research addresses this issue by proposing a Framework for Aerospace
Vehicle Reasoning, shortly called FAVER. FAVER is developed to enable isolation and
root cause identification of faults propagating between multiple systems at the aircraft
level. This is done by involving Digital Twins (DTs) of aircraft systems in order to
emulate interactions between these systems and Reasoning to assess health information
to isolate cascading faults. FAVER currently uses four aircraft systems: i) the Electrical
Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control
System, to demonstrate its ability to provide high level reasoning, which can be used for
troubleshooting in practice. FAVER is also demonstrated for its ability to expand, update,
and scale for accommodating new aircraft systems into the framework along with its
flexibility. FAVER’s reasoning ability is also evaluated by testing various use cases.Transport System
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
A novel approach for No Fault Found decision making
Within aerospace and defence sectors, organisations are adding value to their core corporate offerings through services. These services tend to emphasise the potential to maintain future revenue streams and improved profitability and hence require the establishment of cost effective strategies that can manage uncertainties within value led services e.g. maintenance activities. In large organisations, decision-making is often supported by information processing and decision aiding systems; it is not always apparent whose decision affects the outcome the most. Often, accountability moves away from the designated organisation personnel in unforeseen ways, and depending on the decisions of individual decision makers, the structure of the organisation, or unregulated operating procedures may change. This can have far more effect on the overall system reliability – leading to inadequate troubleshooting, repeated down-time, reduced availability and increased burden on Through-life Engineering Services.
This paper focuses on outlining current industrial attitudes regarding the No Fault Found (NFF) phenomena and identifies the drivers that influence the NFF decision-making process. It articulates the contents of tacit knowledge and addresses a knowledge gap by developing NFF management policies. The paper further classifies the NFF phenomenon into five key processes that must be controlled by using the developed policies. In addition to the theoretical developments, a Petri net model is also outlined and discussed based on the captured information regarding NFF decision-making in organisations. Since NFF decision-making is influenced by several factors, Petri nets are sought as a powerful tool to realise a meta-model capability to understand the complexity of situations. Its potential managerial implications can help describe decision problems under conditions of uncertainty. Finally, the conclusions indicate that engineering processes, which allow decision-making at various maintenance echelons, can often obfuscate problems that then require a systems approach to illustrate the impact of the issue
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Qualitative Adaptive Identification for Powertrain Systems. Powertrain Dynamic Modelling and Adaptive Identification Algorithms with Identifiability Analysis for Real-Time Monitoring and Detectability Assessment of Physical and Semi-Physical System Parameters
A complete chain of analysis and synthesis system identification tools for detectability
assessment and adaptive identification of parameters with physical interpretation
that can be found commonly in control-oriented powertrain models is
presented. This research is motivated from the fact that future powertrain control
and monitoring systems will depend increasingly on physically oriented system
models to reduce the complexity of existing control strategies and open the
road to new environmentally friendly technologies. At the outset of this study
a physics-based control-oriented dynamic model of a complete transient engine
testing facility, consisting of a single cylinder engine, an alternating current dynamometer
and a coupling shaft unit, is developed to investigate the functional
relationships of the inputs, outputs and parameters of the system. Having understood
these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended
algorithms is illustrated with three novel practical applications. These are,
the development of an on-line health monitoring system for engine dynamometer
coupling shafts based on recursive estimation of shaft’s physical parameters, the
sensitivity analysis and adaptive identification of engine friction parameters, and
the non-linear recursive parameter estimation with parameter estimability analysis
of physical and semi-physical cyclic engine torque model parameters. The
findings of this research suggest that the combination of physics-based control oriented
models with adaptive identification algorithms can lead to the development
of component-based diagnosis and control strategies. Ultimately, this work
contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control
for vehicular systems
Fault-detection on an experimental aircraft fuel rig using a Kalman filter-based FDI screen
Reliability is an important issue across industry. This is due to a number of drivers such as the requirement of high safety levels within industries such as aviation, the need for mission success with military equipment, or to avoid monetary losses (due to unplanned outage) within the process and many other industries. The application of fault detection and identification helps to identify the presence of faults to improve mission success or increase up-time of plant equipment. Implementation of such systems can take the form of pattern recognition, statistical and geometric classifiers, soft computing methods or complex model based methods. This study deals with the latter, and focuses on a specific type of model, the Kalman filter.
The Kalman filter is an observer which estimates the states of a system, i.e. the physical variables, based upon its current state and knowledge of its inputs. This relies upon the creation of a mathematical model of the system in order to predict the outputs of the system at any given time. Feedback from the plant corrects minor deviation between the system and the Kalman filter model. Comparison between this prediction of outputs and the real output provides the indication of the presence of a fault. On systems with several inputs and outputs banks of these filters can used in order to detect and isolate the various faults that occur in the process and its sensors and actuators.
The thesis examines the application of the diagnostic techniques to a laboratory scale aircraft fuel system test-rig. The first stage of the research project required the development of a mathematical model of the fuel rig. Test data acquired by experiment is used to validate the system model against the fuel rig. This nonlinear model is then simplified to create several linear state space models of the fuel rig. These linear models are then used to develop the Kalman filter Fault Detection and Identification (FDI) system by application of appropriate tuning of the Kalman filter gains and careful choice of residual thresholds to determine fault condition boundaries and logic to identify the location of the fault. Additional performance enhancements are also achieved by implementation of statistical evaluation of the residual signal produced and by automatic threshold calculation.
The results demonstrate the positive capture of a fault condition and identification of its location in an aircraft fuel system test-rig. The types of fault captured are hard faults such sensor malfunction and actuator failure which provide great deviation of the residual signals and softer faults such as performance degradation and fluid leaks in the tanks and pipes. Faults of a smaller magnitude are captured very well albeit within a larger time range. The performance of the Fault Diagnosis and Identification was further improved by the implementation of statistically evaluating the residual signal and by the development of automatic threshold determination. Identification of the location of the fault is managed by the use of mapping the possible fault permutations and the Kalman filter behaviour, this providing full discrimination between any faults present. Overall the Kalman filter based FDI developed provided positive results in capturing and identifying a system fault on the test-rig
Advances in fuzzy rule-based system for pattern classification
Ph.DDOCTOR OF PHILOSOPH
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