Model-oriented complex safety analysis of dynamical technological systems by method SQMD

Safety analysis is a key activity for the development of railway transportation systems. The analysis is used not only to determine whether new systems do not expose the general public to unduly high levels of risks, but also to inform the development process itself through identification and allocation of appropriate requirements. Whilst inadequate and inaccurate safety analysis may have potentially catastrophic consequences, it is rarely validated in practice. This paper presents a case for validation of safety analysis of the railway systems along with a technical approach to such validation. The approach is based on the concept of Failure Injection and the Lightweight Refinement relation. The paper further argues that the safety analysis validation should be embedded in a wider uncertainty management framework and shows how this can be made consistent with the spirit of the existing safety standards of the railway domain.

The [formula omitted]-compound of a difference–algebraic system

Automatic safety analysis of control systems

Introduction: This article is the result of the research “Analysis of the dynamic systems component of the engineering programs in control and automation and technology in industrial electronics,” developed at the Francisco José de Caldas District University in the years 2022-2023. Problem: The document addresses the lack of a clear and comprehensive structure in the analysis of linear dynamic systems, as well as the absence of an innovative approach to addressing these topics in traditional courses, which hinders their understanding and application in different disciplines. Objective: The objective is to present a novel approach to analyzing linear dynamic systems, providing a complete framework of concepts, relationships, and important tools in this analysis, as well as a literature review based on over seventeen years of experience in courses and studies of this kind. Methodology: A journey of concepts is proposed from signals and systems, through modeling with methods such as black box, white box, gray box, to linear analysis based on examples with calculation of differential equations, state representation, transfer function, and block diagrams. Results: The document aggregates and articulates all the concepts of dynamic systems, along with the relationships and tools used, offering a more practical and intuitive approach to understanding the material. Conclusion: The document provides a comprehensive and articulated view of key concepts in the analysis of linear dynamic systems, highlighting an innovative approach that facilitates their understanding. Its main contribution is to aggregate and articulate these concepts, along with the tools and relationships used, to offer a more practical and clear approach for their study. Originality: The originality lies in proposing a novel and structured approach to analyzing linear dynamic systems, addressing the lack of clarity and completeness in traditional approaches. Limitations: Although the document proposes a novel approach, it does not delve into specific aspects of some topics covered, which could limit the detailed understanding of certain concepts. Deepening: The document could delve into the practical application of the proposed concepts and tools in real cases of dynamic systems, as well as into the comparison with traditional approaches to highlight the differences and advantages of the proposed new approach.

Computational efficiency of the continuous cell-to-cell mapping technique as a function of integration schemes

Recent developments in the modern control systems show a clear trend for increasing integration of a number of safety-related electronic systems - such as railway signalling system and x-by-wire systems - that are replacing traditional mechanical controls. These applications promise improved performance and safety through continuous high-capacity realtime data exchanges and reconfiguration in abnormal situations. However, safety analysis of networked systems is becoming extraordinarily difficult because of the complexity of interactions between components and the number of possible backup modes. The traditional analysis techniques are subjective and do not facilitate reuse, thus, making the safety analysis process time-consuming and error-prone. This paper extends the failure propagation model with a set of temporal operators, in order to demonstrate more accurate representation of practical failure behaviors of networked control systems. Computation laws of temporal operators and combinatorial operators are abstracted to transform complex temporal expressions into a standard form. In this way, the models can be solved automatically. The preliminary design of Communication Based Train Control (CBTC) system for Yizhuang Line in Beijing is used to demonstrate the approach.

This paper presents a novel approach for safety analysis of complex systems. The Bayeisan Networks has been modeled based on failure scenarios contained a large number information of Failure Modes, Effects and Criticality Analysis accumulated in the production and use stages of complex systems. The model's constrution, probabilistic inference, sensetivity annlysis is analysised through a case study on controllable-pitch propeller and shafting system. The results show the validity and convenience of the method and great advantages and applications prospects of Bayesian Networks in safety analysis of complex systems.

In this study stochastic analysis of non-linear dynamical systems under α-stable,
\nmultiplicative white noise has been conducted. The analysis has dealt with a special class of α-stable
\nstochastic processes namely sub-Gaussian white noises. In this setting the governing equation either of the
\nprobability density function or of the characteristic function of the dynamical response may be obtained
\nconsidering the dynamical system forced by a Gaussian white noise with an uncertain factor with α/2-
\nstable distribution. This consideration yields the probability density function or the characteristic function
\nof the response by means of a simple integral involving the probability density function of the system
\nunder Gaussian white noise and the probability density function of the α/2-stable random parameter. Some
\nnumerical applications have been reported assessing the reliability of the proposed formulation. Moreover
\na proper way to perform digital simulation of the sub-Gaussian α-stable random process preventing
\ndynamical systems from numerical overflows has been reported and discussed in detail.

Safety analysis is very important to prevent events that may cause catastrophic accidents in process industries. Although traditional safety assessment methods including fault tree (FT) have been used widely in this field, they still lack in dynamic structure representation and uncertainty processing, which are important for the process safety analysis. In this study, a safety analysis approach based on the probabilistic Petri-net (PPN), which extends the basic Petri-net, is proposed. Probabilities are assigned to the tokens in the Petri-net, and the probability dependencies of events can then be modeled by a PPN. The enabling rule of a transition and the execution rule of an enabled transition are provided, through which the dynamic evolution process of the system can be revealed by the flowing of tokens, and the probabilities corresponding to the occurrence of events can be updated. A case that vapor cloud may overflow from a tank is studied to illustrate the proposed approach. The result is validated by comparing with that of the fault tree analysis.

Chapter 8 Numerical analysis of dynamical systems

In two recent papers, Brayton and Tong developed some significant results which are the basis of a constructive approach in the stability analysis of dynamical systems. Although their algorithm is powerful, it taxes the capabilities of most modern computers when the dimension of a system is high or even when the dimension is moderate in size. In this paper we remove these difficulties to a certain extent by generalizing the work of Brayton and Tong to interconnected dynamical systems. In our approach, the analysis of complex dynamical systems (which may be of high dimension) is accomplished in terms of the qualitative properties of the free subsystems and the interconnections of such systems.

Computer Aided Robustness Analysis of Dynamic Systems

Uncertainty of parameters in control systems was described by interval variables, and some idea of the non-probabilistic reliability method that we proposed in previous papers was used for stability analysis and synthesis of uncertain systems. A new method, called robust reliability method, was presented for quadratic stability analysis and robust stabilization of dynamic systems with interval parameters. In the method, the performance function for robust reliability calculation was established by criterion of quadratic stability, and a solving method in the form of optimization for robust reliability was presented. The proposed stabilization method can take both the required energy supply for control and the control robustness into account. This makes the reasonable strategy of compromising between the control cost and system reliability become possible. By the method, robust reliability measure of the stability degree of uncertain control systems can be provided, and the maximum allowable ranges of the basic parameters can be obtained. The presented method is based on the linear matrix inequality (LMI) approach, and is convenient to implement Three numerical examples were provided to illustrate the validity and efficiency of the presented method.

Safety analysis of mechatronic systems is a time-consuming activity, because of the complexity of these systems that involve different fields of engineering. It is desirable to carry out safety assessment methods as soon as possible in the design process in order to reduce errors, cost and time to market of the system. Our paper addresses this problem by proposing a safety profile that is integrated directly with the modeling elements of the system via SysML, a model-based systems engineering language. Failure modes of each function and each component, their causes, their effects as well as their severity are modeled via stereotypes or tag definitions that extend the existing UML elements. These failure data can be 1) entered directly by systems engineers when possible; and then 2) generated automatically for safety experts' work; and also 3) updated from safety analysis results. Our integrated systems engineering and safety analysis process helps to narrow the gap between these two disciplines by ensuring the consistency in the whole process. A case study with an electromechanical actuator is given to illustrate the process as well as the safety profile.

Safety critical systems are becoming more complex, both in the type of functionality they provide and in the way they are demanded to interact with their environment. Such growing complexity requires an adequate increase in the capability of safety engineers to assess system safety, including analyzing the bahaviour of a system in degraded situations. Formal verification techniques, like symbolic model checking, have the potential of dealing with such a complexity and are more often being used during system design. In this paper we present the FSAP/NuSMV-SA platform, based on the NuSMV2 model checker, that implements known and novel techniques to help safety engineers perform safety analysis. The main functionalities of FSAP/NuSMV-SA include: failure mode definition based on a library of failure modes, fault injection, automatic fault tree construction for monotonic and non-monotonic systems, failure ordering analysis. The goal is to provide an environment that can be used both by design engineers to formally verify a system and by safety engineers to automate certain phases of safety assessment. The platform is being developed within the ESACS project (Enhanced Safety Analysis for Complex Systems), an European-Union-sponsored project in the avionics sector, whose goal is to define a methodology to improve the safety analysis practice for complex systems development.