Model-based Dependability Analysis Research Articles

Updating subsystem-level fault-symptom relationships for Temperature and Humidity Control Systems with redundant functions

As we aim for deep space exploration, supporting vital systems, such as the Temperature and Humidity Control System (THCS) in the Environmental Control and Life Support System (ECLSS), through timely onboard fault detection and diagnosis becomes paramount for mission success. Many existing fault diagnosis approaches assume that the function that models the relationship between faults and associated symptoms (fault-symptom relationships) will remain constant throughout the THCS’ lifetime. Therefore, many of these diagnosis methods are not robust enough to automatically account for changes in fault-symptom relationships as a result of changes in the habitat (e.g., system reconfiguration). The work highlighted here is on (i) surveying existing work on adaptable fault diagnosis methods and (ii) showcasing a real-life case study, in which we identified the need for an automatically adaptable fault diagnosis method. The case study focuses on a reconfigured terrestrial THCS analog, the Heating, Ventilation, and Air Conditioning (HVAC) system, where the original fault-symptom relationship is revealed to be no longer accurate. We then apply current adaptable fault-symptom relationship generation methods, such as Model-Based Dependability Analysis (MBDA) methods and data-driven causal discovery methods. Through this analysis, we detail our procedure in (i) identifying relevant fault-free system information, such as redundancy, to revise fault-symptom relationships used in fault diagnosis and (ii) evaluating the fault diagnosis performance in a THCS with the original and revised fault-symptom relationship. Our contribution lies in identifying the shortcomings of current methods and pinpointing future steps in creating an adaptable fault diagnosis framework. We found that although the MBDA methods can automatically generate fault-symptom relationships given system flow information and fault mode of components, they also required manual revision of the aforementioned information to create fault-symptom relationships that reflect redundancies. On the other hand, we concluded that the causal discovery methods can detect fault-free system information, such as redundancies, that may help us revise fault-symptom relationships, but suspect variables that contribute to redundancies may have to be hand-picked.

Model-based dependability evaluation of a Wireless Communication System in a Virtually Coupled Train Set

A new paradigm based on the concept of virtual coupling of train sets is being elaborated to provide innovative solutions so as to increase efficiency, operational flexibility, line capacity, competitiveness among market players and quality of consumer experience. Train-to-Train (T2T) and Train-to-Ground (T2G) wireless communications will be the backbone for the implementation of the virtual coupling functionalities. In the present paper, we perform a model-based dependability analysis of the wireless communication system under virtual coupling operation. Namely, Stochastic Colored Petri Net (SCPN) models are developed to depict the exchanges of the various information needed under virtual coupling operation. Dependability evaluation is then performed by means of simulation. In particular, the impact of various communication parameters is scrutinized while taking into account different operational scenarios. The obtained results allow the identification of the most impacting aspects on dependability analysis and provide valuable inputs to support the technological choices in terms of communication to implement virtual coupling.

DATAM: A model‐based tool for dependability analysis

SummaryDependability analysis is the main method to evaluate the design of safety‐critical systems, which is able to analyze the source of the faults and find them as early as possible. With the increasing system scale and complexity, Model‐Based Dependability Analysis (MDBA) has become the mainstream, so that it is crucial to provide powerful models that accurately reflect the real systems and easy to be built. However, modeling a system is error‐prone and it is difficult to verify the correctness of the model being built. Therefore, this paper proposes our dependability modeling tool called DATAM (Dependability Analysis Tool of AltaRica Model), which is based on AltaRica, a dataFlow language. We present a method for converting key elements of AltaRica into model/GUI components, thereby ensuring the consistency of the model with the modeling language. GUI‐based operations and rich custom components ensure ease of using DATAM. Besides, some components of the model can be edited directly through the AltaRica script and can be interchanged with GUI components. Finally, DATAM supplies varieties of reliability calculation functions. We demonstrate the ability of DATAM through a case study and compare the results with SimFia education version.

Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks

Dynamic systems exhibit time-dependent behaviours and complex functional dependencies amongst their components. Therefore, to capture the full system failure behaviour, it is not enough to simply determine the consequences of different combinations of failure events: it is also necessary to understand the order in which they fail. Pandora temporal fault trees (TFTs) increase the expressive power of fault trees and allow modelling of sequence-dependent failure behaviour of systems. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. This in turn makes it less viable for use in modern, iterated system design processes, which requires a quicker turnaround and consistency across evolutions. In this paper, we propose for a model-based analysis of temporal fault trees via HiP-HOPS, which is a state-of-the-art model-based dependability analysis method supported by tools that largely automate analysis and optimisation of systems. The proposal extends HiP-HOPS with Pandora, Petri Nets and Bayesian Networks and results to dynamic dependability analysis that is more readily integrated into modern design processes. The effectiveness is demonstrated via application to an aircraft fuel distribution system.

Model-Based Dependability Analysis of Physical Systems with Modelica

Modelica is an innovative, equation-based, and acausal language that allows modeling complex physical systems, which are made of mechanical, electrical, and electrotechnical components, and evaluates their design through simulation techniques. Unfortunately, the increasing complexity and accuracy of such physical systems require new, more powerful, and flexible tools and techniques for evaluating important system properties and, in particular, the dependability ones such as reliability, safety, and maintainability. In this context, the paper describes some extensions of the Modelica language to support the modeling of system requirements and their relationships. Such extensions enable the requirement verification analysis through native constructs in the Modelica language. Furthermore, they allow exporting a Modelica-based system design as a Bayesian Network in order to analyze its dependability by employing a probabilistic approach. The proposal is exemplified through a case study concerning the dependability analysis of a Tank System.

A Framework for Adaptive Fault-Tolerant Execution of Workflows in the Grid: Empirical and Theoretical Analysis

In this paper, we propose and evaluate a framework for fault tolerant workflow execution in Grid environments. Different from previous work in the literature, our system dynamically chooses an appropriate fault tolerance technique while using a user-defined rule-based system. We also provide a generic interface that can be used to add fault tolerance techniques to the framework. The results obtained with real workflows in an experimental Grid environment show that the overhead introduced by our framework in a failure-free execution is, in the worst evaluated case, approximately 10 %. Moreover, we show that, using our framework, workflows are able to execute successfully in the presence of failures and that the framework can dynamically choose an appropriate fault tolerance technique. The main contributions of our work are twofold: the developed framework and the model-based dependability analysis we performed on it. The purpose in carrying out a model-based dependability analysis consists on evaluating the interaction between our framework and the distributed Grid environment beyond the physical limitations of an empirical evaluation. By doing this, we provide means to plan the assurance of QoS in the Grid resource allocation, while applying the fault-tolerance mechanisms we implement in our framework regardless of the underlying middleware.