| 1. |
- Josefsson, Maria, 1979-
(författare)
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Attrition in Studies of Cognitive Aging
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Longitudinal studies of cognition are preferred to cross-sectional stud- ies, since they offer a direct assessment of age-related cognitive change (within-person change). Statistical methods for analyzing age-related change are widely available. There are, however, a number of challenges accompanying such analyzes, including cohort differences, ceiling- and floor effects, and attrition. These difficulties challenge the analyst and puts stringent requirements on the statistical method being used.The objective of Paper I is to develop a classifying method to study discrepancies in age-related cognitive change. The method needs to take into account the complex issues accompanying studies of cognitive aging, and specifically work out issues related to attrition. In a second step, we aim to identify predictors explaining stability or decline in cognitive performance in relation to demographic, life-style, health-related, and genetic factors.In the second paper, which is a continuation of Paper I, we investigate brain characteristics, structural and functional, that differ between suc- cessful aging elderly and elderly with an average cognitive performance over 15-20 years.In Paper III we develop a Bayesian model to estimate the causal effect of living arrangement (living alone versus living with someone) on cog- nitive decline. The model must balance confounding variables between the two living arrangement groups as well as account for non-ignorable attrition. This is achieved by combining propensity score matching with a pattern mixture model for longitudinal data.In paper IV, the objective is to adapt and implement available impu- tation methods to longitudinal fMRI data, where some subjects are lost to follow-up. We apply these missing data methods to a real dataset, and evaluate these methods in a simulation study.
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| 2. |
- Carvalho Bittencourt, André
(författare)
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On Modeling and Diagnosis of Friction and Wear in Industrial Robots
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial robots are designed to endure several years of uninterrupted operation and therefore are very reliable. However, no amount of design effort can prevent deterioration over time, and equipments will eventually fail. Its impacts can, nevertheless, be considerably reduced if good maintenance/service practices are performed. The current practice for service of industrial robots is based on preventive and corrective policies, with little consideration about the actual condition of the system. In the current scenario, the serviceability of industrial robots can be greatly improved with the use of condition monitoring/diagnosis methods, allowing for condition-based maintenance (cbm).This thesis addresses the design of condition monitoring methods for industrial robots. The main focus is on the monitoring and diagnosis of excessive degradations caused by wear of the mechanical parts. The wear processes may take several years to be of significance, but can evolve rapidly once they start to appear. An early detection of excessive wear levels can therefore allow for cbm, increasing maintainability and availability. Since wear is related to friction, the basic idea pursued is to analyze the friction behavior to infer about wear.To allow this, an extensive study of friction in robot joints is considered in this work. The effects of joint temperature, load and wear changes to static friction in robot a joint are modeled based on empirical observations. It is found that the effects of load and temperature to friction are comparable to those caused by wear. Joint temperature and load are typically not measured, but will always be present in applications. Therefore, diagnosis solutions must be able to cope with them.Different methods are proposed which allow for robust wear monitoring. First, a wear estimator is suggested. Wear estimates are made possible with the use of a test-cycle and a friction model. Second, a method is defined which considers the repetitive behavior found in many applications of industrial robots. The result of the execution of the same task in different instances of time are compared to provide an estimate of how the system changed over the period. Methods are suggested that consider changes in the distribution of data logged from the robot. It is shown through simulations and experiments that robust wear monitoring is made possible with the proposed methods.
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| 3. |
- Krysander, Mattias, 1977-
(författare)
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Design and Analysis of Diagnosis Systems Using Structural Methods
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In complex and automated technological processes the effects of a fault can quickly propagate and lead to degradation of process performance or even worse to a catastrophic failure. This means that faults have to be found as quickly as possible and decisions have to be made to stop the propagation of their effects and to minimize process performance degradation. The behavior of the process is affected in different ways by different faults and the fault can be found by ruling out faults for which the expected behavior of the process is not consistent with the observed behavior. In model-based diagnosis, a model describes the expected behavior of the process for the different faults.A device for finding faults is called a diagnosis system. In the diagnosis systems considered here, a number of tests check the consistency of different parts of the model, by using observations of the process. To be able to identify which fault that has occurred, the set of tests that is used must be carefully selected. Furthermore, to reduce the on-line computational cost of running the diagnosis system and to minimize the in general difficult and time-consuming work of tests construction, it is also desirable to use few tests.A two step design procedure for construction of a diagnosis systems is proposed and it provides the means for selecting which tests to use implicitly by selecting which parts of the model that should be tested with each test. Then, the test design for each part can be done with any existing technique for model-based diagnosis.Two different types of design goals concerning the capability of distinguishing faults is proposed. The first goal is to design a sound and complete diagnosis system, i.e., a diagnosis system with the following property. For any observation, the diagnosis system computesexactly the faults that together with the observation are consistent with the model. The second goal is specified by which faults that should be distinguished from other faults, and this is called the desired isolability.Given any of these two design goals, theory and algorithms for selecting a minimum cardinality set of parts of the model are presented. Only parts with redundancy can be used for test construction and a key result is that there exists a sound and complete diagnosis system based on the set of all minimal parts with redundancy in the model. In differentialalgebraic models, it is in general difficult to analytically identify parts with redundancy, because it corresponds to variable elimination or projection. It is formally shown that redundant parts can be found by using a structural approach, i.e., to use only which variables that are included in each equation. In the structural approach, parts with more equations than unknowns are identified with efficient graph-theoretical tools. A key contribution is a new algorithm for finding all minimal parts with redundancy of the model. The efficiency of the algorithm is demonstrated on a truck engine model and compared to the computational complexity of previous algorithms.In conclusion, tools for test selection have been developed. The selection is based on intuitive requirements such as soundness or isolability requirements specified by the diagnosis system designer. This leads to a more straightforward design of diagnosis systems, valuable engineering time can be saved, and the resulting diagnosis systems use minimum number of tests, i.e., the on-line computational complexity of the resulting diagnosis systems become low.
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| 4. |
- Svärd, Carl
(författare)
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Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Fault detection and isolation (FDI) is essential for dependability of complex technical systems. One important application area is automotive systems, where precise and robust FDI is necessary in order to maintain low exhaust emissions, high vehicle up-time, high vehicle safety, and efficent repair. To achieve good performance, and at the same time minimize the need for expensive redundant hardware, model-based FDI is necessary. A model-based FDI-system typically comprises fault detection by means of residual generation and residual evaluation, and finally fault isolation.The overall objective of this thesis is to develop generic and theoretically sound methods for design of model-based FDI-systems. The developed methods are aimed at supporting an automated design methodology. To this end, the methods require a minimum of human interaction. By means of an automated design methodology the overall design process becomes more efficient and systematic, which also contributes to higher quality. These aspects are of particular importance in an industrial context.Design of a model-based FDI-system for a complex real-world system is an intricate task that poses several difficulties and challenges that must be handled by the involved design methods. For instance, modeling of these systems often result in large-scale, non-linear, differential-algebraic models. Furthermore, despite substantial modeling work, models are typically not able to capture the behaviors of systems in all operating modes. This results in model-errors of time-varying nature and magnitude. This thesis develops a set of methods able to handle these issues in a systematic manner.Two methods for model-based residual generation are developed. The two methods handle different stages of the design of residual generators. The first method considers the actual residual generator realization by means of sequential residual generation with mixed causality. The second method considers the problem of how to select an optimal set of residual generators from all possible residual generators that can be created with the first method. Together the two methods enable systematic design of a set of residual generators that fulfills a stated fault isolation requirement. Moreover, the methods are applicable to complex, large-scale, and non-linear differential-algebraic models.Furthermore, a data-driven method for statistical residual evaluation is developed. The method relies on a comparison of the probability distributions of residuals and exploits no-fault data from the system in order to learn the behavior of no-fault residuals. The method can be used to design residual evaluators capable of handling residuals subject to stochastic uncertainties and disturbances caused by for instance time-varying model errors.The developed methods, as well as the potential of an automated design methodology, are evaluated through extensive application studies. To verify their generality, the methods are applied to different automotive systems, as well as a wind turbine system. The performances of the obtained FDI-systems are good in relation to the required engineering effort. Particularly, no specific adaption or no tuning of the methods, or the design methodology, were made.
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| 5. |
- Pernestål, Anna, 1978-
(författare)
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Probabilistic Fault Diagnosis with Automotive Applications
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems.To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation.Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from.Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations.Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks.The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present.The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4. The methods handle probability computations in systems subject to external interventions, and in particular systems that include both instantaneous and non-instantaneous dependencies. They are based on the theory of Bayesian networks, and include event-driven non-stationary dynamic Bayesian networks (nsDBN) and an efficient inference algorithm for troubleshooting based on static Bayesian networks. The framework of nsDBN event-driven nsDBN is applicable to all kinds of problems concerning inference under external interventions.The third contribution area is Bayesian learning from data in the diagnosis application. The contribution is the comparison and evaluation of five Bayesian methods for learning in fault diagnosis in Paper 5. The special challenges in diagnosis related to learning from data are considered. It is shown how the five methods should be tailored to be applicable to fault diagnosis problems.To summarize, the five papers in the thesis have shown how several challenges in automotive diagnosis can be handled by using probabilistic methods. Handling such challenges with probabilistic methods has a great potential. The probabilistic methods provide a framework for utilizing allinformation available, also if it is in different forms and. The probabilities computed can be combined with decision theoretic methods to determine the appropriate action after the discovery of reduced system functionality due to faults.
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| 6. |
- Svärd, Carl, 1981-
(författare)
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Residual Generation Methods for Fault Diagnosis with Automotive Applications
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance.One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation.The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by Differential-Algebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observer-based residual generation for linear DAE-models. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in state-space form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. null-space calculations and equivalence transformations.In the second method, non-linear DAE-models are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system.
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| 7. |
- Tahvili, Sahar
(författare)
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A Decision Support System for Integration Test Selection
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software testing generally suffers from time and budget limitations. Indiscriminately executing all available test cases leads to sub-optimal exploitation of testing resources. Selecting too few test cases for execution on the other hand might leave a large number of faults undiscovered. Test case selection and prioritization techniques can lead to more efficient usage of testing resources and also early detection of faults. Test case selection addresses the problem of selecting a subset of an existing set of test cases, typically by discarding test cases that do not add any value in improving the quality of the software under test. Test case prioritization schedules test cases for execution in an order to increase their effectiveness at achieving some performance goals such as: earlier fault detection, optimal allocation of testing resources and reducing overall testing effort. In practice, prioritized selection of test cases requires the evaluation of different test case criteria, and therefore, this problem can be formulated as a multi-criteria decision making problem. As the number of decision criteria grows, application of a systematic decision making solution becomes a necessity. In this thesis, we propose a tool-supported framework using a decision support system, for prioritizing and selecting integration test cases in embedded system development. The framework provides a complete loop for selecting the best candidate test case for execution based on a finite set of criteria. The results of multiple case studies, done on a train control management subsystem from Bombardier Transportation AB in Sweden, demonstrate how our approach helps to select test cases in a systematic way. This can lead to early detection of faults while respecting various criteria. Also, we have evaluated a customized return on investment metric to quantify the economic benefits in optimizing system integration testing using our framework.
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| 8. |
- Warnquist, Håkan, 1982-
(författare)
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Computer-Assisted Troubleshooting for Efficient Off-board Diagnosis
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This licentiate thesis considers computer-assisted troubleshooting of complex products such as heavy trucks. The troubleshooting task is to find and repair all faulty components in a malfunctioning system. This is done by performing actions to gather more information regarding which faults there can be or to repair components that are suspected to be faulty. The expected cost of the performed actions should be as low as possible.The work described in this thesis contributes to solving the troubleshooting task in such a way that a good trade-off between computation time and solution quality can be made. A framework for troubleshooting is developed where the system is diagnosed using non-stationary dynamic Bayesian networks and the decisions of which actions to perform are made using a new planning algorithm for Stochastic Shortest Path Problems called Iterative Bounding LAO*.It is shown how the troubleshooting problem can be converted into a Stochastic Shortest Path problem so that it can be efficiently solved using general algorithms such as Iterative Bounding LAO*. New and improved search heuristics for solving the troubleshooting problem by searching are also presented in this thesis.The methods presented in this thesis are evaluated in a case study of an auxiliary hydraulic braking system of a modern truck. The evaluation shows that the new algorithm Iterative Bounding LAO* creates troubleshooting plans with a lower expected cost faster than existing state-of-the-art algorithms in the literature. The case study shows that the troubleshooting framework can be applied to systems from the heavy vehicles domain.
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