Model-based software diagnosis

Software development is a complex process in which it is all too easy to introduce errors or faults that can be extremely difficult to identify . However , there has been little research into the use of intelligent diagnostic techniques for software ( as compared to hardware , for example ). Therefore , applying such techniques to software is potentially extremely useful . In many cases the presence of a bug is only identified when an invalid result is observed . However , the task of identifying the software elements that may have contributed to this result can be a time - consuming and tedious task , but must be performed before the actual root cause can be identified and a repair effected . This operation is akin to fault localization within hardware diagnosis and has been the subject of extensive research within the model - based community . This article presents a technique that minimizes the tedious task of fault localization with the software system , leaving the developer to concentrate on identifying the root cause and remedial action . In particular , we treat diagnosis as a trial with witnesses for the prosecution and defense . The result is a diagnostic trial that uses the source code of a system as its model and knowledge of valid and invalid results ( victims ) to identify a set of suspects for a developer to investigate further .     

Model-based cognitive diagnosis

This paper considers the problem of cognitive diagnosis as an instance of general diagnosis, as studied in artificial intelligence. Cognitive diagnosis is the process of inferring a cognitive state from observations of performance. It is thus a key component of any system which attempts to build a dynamic model of the user of that system. Many issues in cognitive diagnosis, previously discussed informally, are mapped onto formal techniques, with consequent increased clarity and rigour. But it is concluded that the general theories for diagnosis must be broadened to fully encompass the problems of cognitive diagnosis.     

Model-based diagnosis of continuous static systems

Research on model-based diagnosis of technical systems has grown enormously in the last few years, producing new basic tools, new algorithms and also some applications. However, the majority of research has dealt with systems described by variables ranging in discrete domains (e.g., digital circuits), and only few attempts have been made at applying such techniques to continuous domains. Continuous systems are characterized by additional problems, such as the unavoidable sensor errors and the need for using more complex models which may consist of simultaneous non-linear equations. The distinctive feature of the approach we present in this paper is the integration of techniques well known in the field of numerical analysis and statistics (e.g., the solution of non-linear systems and the error propagation) with a dependency-recording technique based on ordering the equations and the variables of the model.     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

Model-based diagnosis or reasoning from first principles

Although the basics of model-based diagnosis are well known, creating appropriate domain-specific models is still a challenge. Nevertheless, empirical results show that model-based diagnosis techniques are now appropriate for medium-size real-world problems.     

Model-based diagnosis techniques for Internet delay diagnosis with dynamic routing

In this paper we propose a model-based approach to diagnose latencies in computer networks. We formalize this problem as a model-based diagnosis (MBD) problem and propose a range of methods to solve it. Three solution approaches are proposed: a conflict-directed approach, a constraint satisfaction approach and a linear programming approach. We discuss the pros and cons of these approaches and describe which approaches are suited to handle which network routing policies. In particular we handle this work networks with static routing policies, where there exists a static route between every pair of end-users, as well as two common types of dynamic routing policies, where information between a pair of end users may pass via more than a single route. The performance of the proposed approaches is demonstrated experimentally on two domains: the standard NS2 network simulator and on parts of the Internet topology obtained from the Route Views project. Both able to find diagnoses fast for network models with 1,000 nodes.     

Model-based diagnosis of systems emphasizing a holistic approach

Diagnosis of technical systems is very important both from the technical and economic point of view. Monitoring and diagnosis start with the observation of symptoms, which depend on the fast time dynamics and on the system's evolution based on the slow time coordinate. Model-based diagnosis can be performed by symptoms directly and by a holistic mathematical model of the entire system decomposed into subsystems, whereas the submodels of the related subsystems can be of different accuracies dependent on their goals. If these models are adjusted at every lifetime required for condition assessment of the operating system/system in service, and if the models are verified and validated, then they represent the best knowledge base available for diagnosis. Adaptation of mathematical models uses system identification tools which can produce verified, validated and usable mathematical models. These models serve for fault detection, localization, assessment and cause finding, for trend prediction and decisionmaking for further use of the system. Consequently, the procedure described combined with an extended knowledge base of the limit states etc. of the system can be seen as an intelligent diagnostic method.     

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme.     

Model-based diagnosis for proton exchange membrane fuel cells

Proton Exchange Membrane Fuel Cell (PEMFC) systems are more and more presented as a good alternative to current energy converters such as internal combustion engines. They suffer however from insufficient reliability and durability for stationary and transport applications. Reliability and lifetime may be improved by suitable fault detection and localization.Traditionally, fault diagnosis in fuel cell systems needs the knowledge of number of parameters, which might require a special inner parameter monitoring setup. This is difficult, even impossible with respect to fuel cell stacks geometry. Moreover, with respect to the transportation application that aims at minimizing the embedded instrumentation, simple diagnosis methods involving non-intrusive and easy-to-monitor parameters are highly desired in PEMFC systems.We present in this paper a flooding diagnosis procedure based on black-box model. This diagnosis method allows automating the flooding diagnosis and the parameters used are minimal, low-cost and simple to monitor. The model inputs are some variables that are critical for water management in the PEMFC and consequently for fuel cell performances while the output is a variable that can be monitored in a non-intrusive way and can be used to detect flooding (namely pressure drop through the cathode).The flooding diagnosis procedure is based on the analysis of a residual obtained from the comparison between an experimental and an estimated pressure drop. The estimation of this latter is ensured by an artificial Neural Network that has been trained with flooding-free data. Fault detection is obtained by means of a residual analysis.It has been successfully tested under different experimental conditions including non-flooding and deliberately induced flooding as well as a succession of the two states of health. A proposition to include drying out problems is given as perspective to this work.     

Model-based diagnosis of spreadsheet programs: a constraint-based debugging approach

Spreadsheet programs are probably the most successful example of end-user software development tools and are used for a variety of purposes. Like any type of software, they are prone to error, in particular as they are usually developed by non-programmers. While various techniques exist to support the developer in finding errors in procedural programs, the tool support for spreadsheet debugging is still limited. In this paper, we show how techniques from model-based diagnosis can be applied and extended for spreadsheet debugging by translating the relevant parts of a spreadsheet to a constraint satisfaction problem. We additionally propose both problem-specific and generalizable extensions to the classical diagnosis algorithms which help to detect potential problems in a spreadsheet based on user-provided test cases more efficiently. The proposed techniques were integrated into a modular framework for spreadsheet debugging and evaluated with respect to scalability based on a number of real-world and artificially created spreadsheets. An additional error detection exercise involving 24 subjects was performed to assess the general applicability of such advanced spreadsheet debugging techniques for end users.     

Towards Model-based Methods for Developing Model-based Systems

Model-based reasoning (MBR) is a means of reasoning about models of all kinds, as appropriate to the task at hand. This includes adaptation of models in response to changes in a problem-solving context or task goals. Thus, MBR exemplifies the characteristics of a smart adaptive system. Constructing appropriate models and matching them to the best inference engines requires a means of describing or defining models. This can be achieved by means of a set of generic model properties. As well as defining models one may decompose the problem domain into a number of tasks to be performed. It then becomes possible to design appropriate models for the domain by mapping these tasks to the model properties. This mapping can be instantiated procedurally, however more generality will result if a model-based approach is taken. As a step towards that goal quality function deployment is investigated as a suitable design method.     

Model-based reasoning

In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper contextual and social support. More specifically, we first will identify some key elements of the structure and function of mental models in contrast to schemas. Next, these key elements of modeling will be used to generate some conjectures about the foundations of model-based reasoning. In the next section, we will describe the learning-dependent progression of mental models as a suitable approach for understanding the basics of deductive and inductive reasoning based on models as “tools for thought.” The rationale of mental models as tools for reasoning will be supported by empirical research to be described in a particular section of this paper. Finally, we will turn to the instructional implications of model-based reasoning by discussing appropriate instructional methods to affect the construction of mental models for performing deductive and inductive reasoning.     

Model-based codesign

Hardware-software codesign has been a research topic since the beginning of this decade (1990s), but only now are structured methods emerging that focus on automating design. Unfortunately, to date most codesign approaches leverage performance from individual hardware and software tools, rather than enforcing a structured integration of hardware and software systems simultaneously. A few frameworks have successfully done this integration and have the potential for significant benefits, including reduced time to market, smaller scale design, better likelihood of component reuse, and maximum use of processing power. The article describes a codesign approach that lets developers create models of a formal system representation independently of the hardware and software implementation. The authors' framework, which targets embedded systems, lets developers use simulation based modeling to explore the feasibility of virtual prototypes and then interactively map the specification onto a software-hardware architecture     

Model-based specification

The classical specification formalism involving pre- and postconditions expressed in program variables cannot directly be applied to the specification of classes, interfaces, components, and design patterns, since the program variables involved are mostly invisible outside of (dynamically selected) implementation code. A more useful specification formalism, operating at the problem domain level rather than at the implementation level, is proposed and it is shown how this can be used for Dijkstra-style derivation of object-oriented code.     

Model-based diagnosis and fault tolerant control for ensuring torque functional safety of pedal-by-wire systems

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems.     

A novel method for pulmonary embolism detection in CTA images

In this paper, we propose a new computer-aided detection (CAD) – based method to detect pulmonary embolism (PE) in computed tomography angiography images (CTAI). Since lung vessel segmentation is the main objective to provide high sensitivity in PE detection, this method performs accurate lung vessel segmentation. To concatenate clogged vessels due to PEs, the starting region of PEs and some reference points (RPs) are determined. These RPs are detected according to the fixed anatomical structures. After lung vessel tree is segmented, the region, intensity, and size of PEs are used to distinguish them. We used the data sets that have heart disease or abnormal tissues because of lung disease except PE in this work. According to the results, 428 of 450 PEs, labeled by the radiologists from 33 patients, have been detected. The sensitivity of the developed system is 95.1% at 14.4 false positive per data set (FP/ds). With this performance, the proposed CAD system is found quite useful to use as a second reader by the radiologists.     

Model-based process redesign

A method is presented for the model-based redesign of enterprises. It consists of two main steps, the initialization step and the optimization step. During the initialization step, the processes are modelled and analysed. During the optimization step, the optimal configuration is found for the possible modifications of the processes. Emphasis is placed on the process analysis. The proposed approach is based on activity chains which describe the set of activities needed for the achievement of a given goal, and their execution order necessary to guarantee a low processing time. An algorithm is presented for filtering such activity chains from an enterprise model. It is based on graph theory and corresponds to the solution of an optimization problem, whereby the sum of weights of all edges belonging to the solution graph should be maximized under some given constraints. Because of the high complexity of the problem, some complexity-reducing measures are presented, measures that arise from the earlier problem analysis.