Engineering failure analysis and design optimisation with HiP-HOPS

The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved

Inductive Reference Modelling Based on Simulated Social Collaboration

Organizations nowadays possess huge repositories of process models. Inductive reference modelling can save costs and time by reusing process parts of process models belonging to a common domain. The inductive development of a reference model for a large corpus of process models is a difficult problem. Quite a few, primarily heuristic approaches have been proposed to the research community that require an approximate matching between the single processes. With our approach, we introduce a new concept that brings in for the first time an abstract efficiency simulation of the social collaboration around knowledge-based process models. A reference model is assembled featuring at least the topological minimum requirements to be significantly more efficient than the input process models. Our evaluation indicates that the approach is able to generate reference process models that are more efficient than the input process models and at least as a reference model designed by an expert

AN EXECUTION-SEMANTIC APPROACH TO INDUCTIVE REFERENCE MODEL DEVELOPMENT

Reference models are a cost- and time-saving approach for the development of new models. As induc-tive strategies are capable of automatically deriving a potential reference process model from a col-lection of existing process models, they have gained attention in current research. A number of prom-ising approaches can be found in recent publications. However, all existing methods rely on graph-based similarity measures to identify commonalities between input models. Since behaviourally simi-lar process models can have different graphical structures, those approaches are unable to find cer-tain commonalities. To overcome these shortcomings, we propose a new approach to inductive refer-ence model development based on an execution-semantic similarity measure. Since a naïve solution to the intuitive idea does not yield productive results, the proposed approach is rather elaborate. By cap-turing the commonalities of the input models in a behavioural profile, we are able to derive a refer-ence model subsuming the input models’ semantics instead of their structure. In our contribution, this approach is outlined, implemented and evaluated in three different scenarios. As the evaluations show, it is capable of handling complex process models and overcome most restrictions that structural ap-proaches pose. Thus, it introduces a new level of flexibility and applicability to inductive reference modelling

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field