329,202 research outputs found
Probabilistic Model-Based Safety Analysis
Model-based safety analysis approaches aim at finding critical failure
combinations by analysis of models of the whole system (i.e. software,
hardware, failure modes and environment). The advantage of these methods
compared to traditional approaches is that the analysis of the whole system
gives more precise results. Only few model-based approaches have been applied
to answer quantitative questions in safety analysis, often limited to analysis
of specific failure propagation models, limited types of failure modes or
without system dynamics and behavior, as direct quantitative analysis is uses
large amounts of computing resources. New achievements in the domain of
(probabilistic) model-checking now allow for overcoming this problem.
This paper shows how functional models based on synchronous parallel
semantics, which can be used for system design, implementation and qualitative
safety analysis, can be directly re-used for (model-based) quantitative safety
analysis. Accurate modeling of different types of probabilistic failure
occurrence is shown as well as accurate interpretation of the results of the
analysis. This allows for reliable and expressive assessment of the safety of a
system in early design stages
A PVS-Simulink Integrated Environment for Model-Based Analysis of Cyber-Physical Systems
This paper presents a methodology, with supporting tool, for formal modeling and analysis of software components in cyber-physical systems. Using our approach, developers can integrate a simulation of logic-based specifications of software components and Simulink models of continuous processes. The integrated simulation is useful to validate the characteristics of discrete system components early in the development process. The same logic-based specifications can also be formally verified using the Prototype Verification System (PVS), to gain additional confidence that the software design complies with specific safety requirements. Modeling patterns are defined for generating the logic-based specifications from the more familiar automata-based formalism. The ultimate aim of this work is to facilitate the introduction of formal verification technologies in the software development process of cyber-physical systems, which typically requires the integrated use of different formalisms and tools. A case study from the medical domain is used to illustrate the approach. A PVS model of a pacemaker is interfaced with a Simulink model of the human heart. The overall cyber-physical system is co-simulated to validate design requirements through exploration of relevant test scenarios. Formal verification with the PVS theorem prover is demonstrated for the pacemaker model for specific safety aspects of the pacemaker design
Fault Propagation Analysis on the Transaction-Level Model of an Acquisition System with Bus Fallback Modes
The early fault analysis is mandatory for safety critical systems, which are required to operate safely even on the presence of faults. System design methodologies tackle the early design and verification of systems by allowing several abstraction for their models, but still offer only digital bit faults as fault models. Therefore we develop a signal fault model for the Transaction-Level Modeling. We extend the TLM generic payload by the signal characteristics: Voltage level, delay, slope time and glitches. In order to analyze and process these, a TLM bus model is created, with which signal faults can be detected and translated to data failures. Furthermore, inserting this bus in an acquisition system and implementing fallback modes for the bus operation, the propagation of the signal faults through the system can be assessed. Simulating this model using probability distributions for the different signal faults, 5516 faults have been generated. From these, 5143 have been recovered, 239 isolated and 134 turned into failures
ADGS-2100 Adaptive Display and Guidance System Window Manager Analysis
Recent advances in modeling languages have made it feasible to formally specify and analyze the behavior of large system components. Synchronous data flow languages, such as Lustre, SCR, and RSML-e are particularly well suited to this task, and commercial versions of these tools such as SCADE and Simulink are growing in popularity among designers of safety critical systems, largely due to their ability to automatically generate code from the models. At the same time, advances in formal analysis tools have made it practical to formally verify important properties of these models to ensure that design defects are identified and corrected early in the lifecycle. This report describes how these tools have been applied to the ADGS-2100 Adaptive Display and Guidance Window Manager being developed by Rockwell Collins Inc. This work demonstrates how formal methods can be easily and cost-efficiently used to remove defects early in the design cycle
Handling consistency between safety and system models
Safety analyses are of paramount importance for the development of embedded systems. In order to perform these analyses, safety engineers use different modeling techniques, such as, for instance, Fault Trees or Reliability Block Diagrams. One of the industrial development process challenges today is to ensure the consistency between safety models and system architectures. Model Based Safety Analysis (MBSA) is one of the newest modeling methods, which promises to ease the exchange of information between safety engineers and system designers. The aim of this article is to discuss an approach to manage the consistency between MBSA models and system architectures.NOur study is based on the experimentation of the co-design of an RPAS (Remotely Piloted Aircraft System) involving system design and safety teams during the early conception phases of an industrial development process. We simulate the process of exchange between the system design and the safety assessment with the constraint of creating safety models close to system architecture. We identify significant exchange points between these two activities. We also discuss the encountered problems and perspectives on the possibility to ensure the consistency between safety and system models
Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress
The impact performance of the wheel during wheel development must be ensured
through a wheel impact test for vehicle safety. However, manufacturing and
testing a real wheel take a significant amount of time and money because
developing an optimal wheel design requires numerous iterative processes of
modifying the wheel design and verifying the safety performance. Accordingly,
the actual wheel impact test has been replaced by computer simulations, such as
Finite Element Analysis (FEA), but it still requires high computational costs
for modeling and analysis. Moreover, FEA experts are needed. This study
presents an aluminum road wheel impact performance prediction model based on
deep learning that replaces the computationally expensive and time-consuming 3D
FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and
barrier mass value used for wheel impact test are utilized as the inputs to
predict the magnitude of maximum von Mises stress, corresponding location, and
the stress distribution of 2D disk-view. The wheel impact performance
prediction model can replace the impact test in the early wheel development
stage by predicting the impact performance in real time and can be used without
domain knowledge. The time required for the wheel development process can be
shortened through this mechanism
Modeling and Analyzing Cyber-Physical Systems Using Hybrid Predicate Transition Nets
Cyber-Physical Systems (CPSs) are software controlled physical devices that are being used everywhere from utility features in household devices to safety-critical features in cars, trains, aircraft, robots, smart healthcare devices. CPSs have complex hybrid behaviors combining discrete states and continuous states capturing physical laws. Developing reliable CPSs are extremely difficult. Formal modeling methods are especially useful for abstracting and understanding complex systems and detecting and preventing early system design problems. To ensure the dependability of formal models, various analysis techniques, including simulation and reachability analysis, have been proposed in recent decades. This thesis aims to provide a unified formal modeling and analysis methodology for studying CPSs.
Firstly, this thesis contributes to the modeling and analysis of discrete, continuous, and hybrid systems. This work enhances modeling of discrete systems using predicate transition nets (PrTNs) by fully realizing the underlying specification through incorporating the first-order logic with set theory, improving the type system, and providing incremental model composition. This work enhances the technique of analyzing discrete systems using PrTN by improving the simulation algorithm and its efficient implementation. This work also improves the analysis of discrete systems using SPIN by providing a more accurate and complete translation method.
Secondly, this work contributes to the modeling and analysis of hybrid systems by proposing an extension of PrTNs, hybrid predicate transition nets (HPrTNs). The proposed method incorporates a novel concept of token evolution, which nicely addresses the continuous state evolution and the conflicts present in other related works. This work presents a powerful simulation capability that can handle linear, non-linear dynamics, transcendental functions through differential equations. This work also provides a complementary technique for reachability analysis through the translation of HPrTN models for analysis using SpaceEx
Compositional Probabilistic Analysis of Temporal Properties over Stochastic Detectors
Run-time monitoring is a vital part of safety-critical systems. However, early-stage assurance of monitoring quality is currently limited: it relies either on complex models that might be inaccurate in unknown ways, or on data that would only be available once the system has been built. To address this issue, we propose a compositional framework for modeling and analysis of noisy monitoring systems. Our novel 3-value detector model uses probability spaces to represent atomic (non-composite) detectors, and it composes them into a temporal logic-based monitor. The error rates of these monitors are estimated by our analysis engine, which combines symbolic probability algebra, independence inference, and estimation from labeled detection data. Our evaluation on an autonomous underwater vehicle found that our framework produces accurate estimates of error rates while using only detector traces, without any monitor traces. Furthermore, when data is scarce, our approach shows higher accuracy than non-compositional data-driven estimates from monitor traces. Thus, this work enables accurate evaluation of logical monitors in early design stages before deploying them
Early Detection of Design Errors in the Life Cycle of Unmanned Aerial Vehicles: A SysML Approach
The widespread of Unmanned Aerial Vehicles (UAVs) in various application domains has questioned the design methods used by UAV manufacturers. Migration from document centric approaches to Model-Based ones has stimulated research work on modeling languages and tools that reduce cost development and time to market. Among the various benefits one may expect from using a Model-Based System Engineering approach, the paper essentially considers a model as a reference for early detection of design errors in the life cycle of UAVs. The paper proposes designers to model the UAV in SysML and to use the free software TTool for safety analysis. TTool includes a SysML model editor, a model simulator and formal verification modules that rely safety analysis on mathematics rather than chance. The method associated with SysML and TTool is applied to a UAV in charge of taking pictures
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