A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Measurements, Models, Systems and Design

531 s.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller

Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.<br /

Compositional dependability analysis of dynamic systems with uncertainty

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

Power system fault analysis based on intelligent techniques and intelligent electronic device data

This dissertation has focused on automated power system fault analysis. New contributions to fault section estimation, protection system performance evaluation and power system/protection system interactive simulation have been achieved. Intelligent techniques including expert systems, fuzzy logic and Petri-nets, as well as data from remote terminal units (RTUs) of supervisory control and data acquisition (SCADA) systems, and digital protective relays have been explored and utilized to fufill the objectives. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Data from RTUs of SCADA systems and digital protective relays are used as inputs. Experiential tests have shown that the proposed approach is able to perform accurate fault section estimation under complex scenarios. The evaluation of protection system performance involves issues of data acquisition, prediction of expected operations, identification of unexpected operations and diagnosis of the reasons for unexpected operations. An automated protection system performance evaluation application has been developed to accomplish all the tasks. The application automatically retrieves relay files, processes relay file data, and performs rule-based analysis. Forward chaining reasoning is used for prediction of expected protection operation while backward chaining reasoning is used for diagnosis of unexpected protection operations. Lab tests have shown that the developed application has successfully performed relay performance analysis. The challenge of power system/protection system interactive simulation lies in modeling of sophisticated protection systems and interfacing the protection system model and power system network model seamlessly. An approach which utilizes the "compiled foreign model" mechanism of ATP MODELS language is proposed to model multifunctional digital protective relays in C++ language and seamlessly interface them to the power system network model. The developed simulation environment has been successfully used for the studies of fault section estimation and protection system performance evaluation

Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

This research extends the knowledge of live-virtual-constructive (LVC) and distributed virtual simulations (DVS) through a detailed analysis and characterization of their underlying computing architecture. LVCs are characterized as a set of asynchronous simulation applications each serving as both producers and consumers of shared state data. In terms of data aging characteristics, LVCs are found to be first-order linear systems. System performance is quantified via two opposing factors; the consistency of the distributed state space, and the response time or interaction quality of the autonomous simulation applications. A framework is developed that defines temporal data consistency requirements such that the objectives of the simulation are satisfied. Additionally, to develop simulations that reliably execute in real-time and accurately model hierarchical systems, two real-time design patterns are developed: a tailored version of the model-view-controller architecture pattern along with a companion Component pattern. Together they provide a basis for hierarchical simulation models, graphical displays, and network I/O in a real-time environment. For both LVCs and DVSs the relationship between consistency and interactivity is established by mapping threads created by a simulation application to factors that control both interactivity and shared state consistency throughout a distributed environment

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)