Automatic Generation of RAMS Analyses from Model-based Functional Descriptions using UML State Machines

In today's industrial practice, safety, reliability or availability artifacts such as fault trees, Markov models or FMEAs are mainly created manually by experts, often distinctively decoupled from systems engineering activities. Significant efforts, costs and timely requirements are involved to conduct the required analyses. In this paper, we describe a novel integrated model-based approach of systems engineering and dependability analyses. The behavior of system components is specified by UML state machines determining intended/correct and undesired/faulty behavior. Based on this information, our approach automatically generates different dependability analyses in the form of fault trees. Hence, alternative system layouts can easily be evaluated. The same applies for simple variations of the logical input-output relations of logical units such as controllers. We illustrate the feasibility of our approach with the help of simple examples using a prototypical implementation of the presented concepts

Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components' failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.Comment: This paper is an extended version of the SETTA 2019 paper, Springer-Verla

Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event

A Formal Transformation Method for Automated Fault Tree Generation from a UML Activity Model

Fault analysis and resolution of faults should be part of any end-to-end system development process. This paper is concerned with developing a formal transformation method that maps control flows modeled in UML Activities to semantically equivalent Fault Trees. The transformation method developed features the use of propositional calculus and probability theory. Fault Propagation Chains are introduced to facilitate the transformation method. An overarching metamodel comprised of transformations between models is developed and is applied to an understood Traffic Management System of Systems problem to demonstrate the approach. In this way, the relational structure of the system behavior model is reflected in the structure of the Fault Tree. The paper concludes with a discussion of limitations of the transformation method and proposes approaches to extend it to object flows, State Machines and functional allocations.Comment: 1st submission made to IEEE Transactions on Reliability on 27-Nov-2017; 2nd submission (revision) made on 27-Apr-2018. This version is the 2nd submission. 20 pages, 11 figure

A formal transformation method for automated fault tree generation from a UML activity model

IEEE Fault analysis and resolution of faults should be part of any end-to-end system development process. This paper is concerned with developing a formal transformation method that maps control flows modeled in unified modeling language activities to semantically equivalent fault trees. The transformation method developed features the use of propositional calculus and probability theory. Fault propagation chains are introduced to facilitate the method. An overarching metamodel comprised of transformations between models is developed and is applied to an understood traffic management system of systems problem to demonstrate the approach. In this way, the relational structure of the system behavior model is reflected in the structure of the fault tree. The paper concludes with a discussion of limitations of the transformation method and proposes approaches to extend it to object flows, state machines, and functional allocations

Model-Based Availability Evaluation of Composed Web Services, Journal of Telecommunications and Information Technology, 2014, nr 4

Web services composition is an emerging software development paradigm for the implementation of distributed computing systems, the impact of which is very relevant both in research and industry. When a complex functionality has to be delivered on the Internet, a service integrator can produce added value by delivering more abstract and complex services obtained by composition of existing ones. But while isolated services availability can be improved by tuning and reconguring their hosting servers, with Composed Web Services (CWS) basic services must be taken as they are. In this case, it is necessary to evaluate the composition effects. The authors propose a high-level analysis methodology, supported by a tool, based on the transformation of BPEL descriptions of CWS into models based on the fault tree availability evaluation formalism that enables a modeler, unfamiliar with the underlying combinatorial probabilistic mathematics, to evaluate the availability of CWS, given components availability and expected execution behavior

Model-Based Safety Analysis

System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical

Formal transformation methods for automated fault tree generation from UML diagrams

With a growing complexity in safety critical systems, engaging Systems Engineering with System Safety Engineering as early as possible in the system life cycle becomes ever more important to ensure system safety during system development. Assessing the safety and reliability of system architectural design at the early stage of the system life cycle can bring value to system design by identifying safety issues earlier and maintaining safety traceability throughout the design phase. However, this is not a trivial task and can require upfront investment. Automated transformation from system architecture models to system safety and reliability models offers a potential solution. However, existing methods lack of formal basis. This can potentially lead to unreliable results. Without a formal basis, Fault Tree Analysis of a system, for example, even if performed concurrently with system design may not ensure all safety critical aspects of the design. [Continues.]</div

Journal of Telecommunications and Information Technology, 2014, nr 4

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