Combining Real-Time Model-Checking and Fault Tree Analysis

This paper presents an augmented framework for analyzing Safety Critical Systems (SCSs) formally. Due to high risk of failure, development process of SCSs is required more attention. Model driven approaches are the one of ways to develop SCSs for accomplishing critical and complex function what SCSs are supposed to do. Two model driven approaches: Unified Modeling Language (UML) and Formal Methods are combined in proposed framework which enables the analysis, designing and testing safety properties of SCSs more rigorously in order to reduce the ambiguities and enhance the correctness and completeness of SCSs. A real time case study has been discussed in order to validate the proposed framework.

The aim of this paper is introducing a method based on Fuzzy Time-To Failure (FTTF) to improve reliability analysis of complex engineering systems based on fault tree analysis. This method focuses on the quantitative part of fault trees (either static or dynamic) analysis and will compute failure probabilities. FTTF model is developed to estimate the reliability of system and solve aforetime methods problems. The presented FTTF model is able to figure out any construction consist of static and dynamic gates with FTTF distributions integrated on Fuzzy Monte Carlo Simulation (FMCS) techniques to analyzing Possibilistic functions associated with the fuzzy probability distributions for each basic event. Using fuzzy algorithm, gates FTTF are generated, and Top-event TTF evaluated. Some case studies are used to demonstrate the priority of this method in exact evaluation in compared with other solving methods (like: BN, Analytical solution, Markov chain and traditional fuzzy fault tree modeling), but has much less effort while having higher accuracy. Finally, this model is implemented in an Emergency Detection System (EDS) which is a useful system in aerospace and space applications.

Several advances in fault tree models have been developed as an aid in the analysis of computer systems. Such advances include the ability to model sequence dependencies, the application of fault trees to the analysis of hardware and software fault tolerant systems, the ability to include details of the recovery process in the fault tree solution, and the use of modularization and binary decision diagrams as an aid in solution. In this paper we present a software package (DIFtree) that implements these advances into a single methodology that can solve both dynamic and static fault trees, and which is applicable to the analysis of hardware, software and humanware in complex computer-based systems.

Risk and uncertainty are integral parts of modern technology, and they must be managed effectively to allow the development of reliable, high‐quality products. Because so many facets of technology and society involve risk and uncertainty, it is essential that risk management be handled in a systematic manner. Fault‐tree analysis is one of the principal methods used in the analysis of systems’safety. Its detailed and systematic deductive structure makes it a valuable tool for design and diagnostic purposes. Point probability and the minimization of the expected failure probability have, until recently, dominated fault‐tree analysis. A methodology that incorporates uncertainty analysis, conditional expected risk, and multiple objectives with fault‐tree analysis is presented. A computer software package termed the “Distribution Analyzer and Risk Evaluator (DARE) Using Fault Trees,” which translates the new methodology into a working decision‐support system, is developed. DARE Using Fault Trees is a flexible computer code that is capable of analyzing the risk of the overall system in terms of the probability density function of failure probability. Emphasis is placed on the uncertainty and risk of extreme events. A comparative study between existing codes for fault‐tree analysis and DARE demonstrates the strengths of the methodology. A case study for NASA's solid rocket booster is used to perform the comparative analysis.

Fault trees constitute one of the essential formalisms for static safety analysis of various industrial systems. Dynamic fault trees (DFT) enrich the formalism by support for time-dependent behaviour, e.g., repairs or dynamic dependencies. This enables more realistic and more precise modelling, and can thereby avoid overly pessimistic analysis results. But analysis of DFT is so far limited to substantially smaller models than those required for instance in the domain of nuclear power safety. This paper considers so called SD fault trees, where the user is free to express each equipment failure either statically, without modelling temporal information, or dynamically, allowing repairs and other timed interdependencies. We introduce an analysis algorithm for an important subclass of SD fault trees. The algorithm employs automatic abstraction techniques effectively, and thereby scales similarly to static analysis algorithms, albeit allowing for a more realistic modelling and analysis. We demonstrate the applicability of the method by an experimental evaluation on fault trees of nuclear power plants.

This paper outlines the concepts for a systematic approach to the safety analysis of chemical processing systems. A procedure for automatically generating fault trees is presented. The fault trees describe nearly all the failure modes for the system under analysis. The fault tree generation procedure uses information on (1) the description of the system (detailed flowsheet), (2) physical and chemical properties of materials in and around the system, and (3) unit models which describe the behavior of the units within the system and which are assembled to describe the behavior of the complete system. The unit models are connected to form an information flow structure for the complete processing system. Unit failure models are also defined for common chemical units. By systematically defining hazard states and searching the information flow structure for the system, it is possible to generate fault trees for the complete process. An analysis of the fault trees can reveal the important failure modes for the process.

Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures.

Quality assurance is a critical aspect in the production systems, affecting product quality and safety. Defects and failures of manufactured components will diminish overall product quality, which could vulnerably risk consumer safety. This study focuses on quality assurance analysis of train component manufacturing systems. According to the quality control data, the number of defects recorded was about 10-12, on average, for each wagon produced. The defect mainly occurred while making the underframes, car body, and even the small components. This led to the tardiness of product delivery for 1-2 months. This study aims to analyze non-conformance report data and identify the potential failure modes, potential effects, and root causes. To do so, we integrated systematically FMEA (Failure Mode and Effect Analysis) and FTA (Fault Tree Analysis). First, RPN (Risk Priority Number) score was calculated to determine risk priority. Second, Pareto analysis was performed to select defects that most contributing to overall failures, which were then analyzed using FTA to obtain root causes. The results show that 8 defects exceed the critical RPN score of 209. Materials and personnel are identified as two major contributor failure events from three selected defects. The recommendation for further improvements is provided based on various defect categories to prevent similar defects. The findings demonstrate that the combined use of FMEA and FTA is effective in identifying failures and root causes within complex and long production cycle systems.

Limitations of traditional tools for beyond design basis external hazard PRA

While offshore salt caverns, as part of the carbon capture and storage (CCS) systems, could offer significant advantages in storage carbon dioxide, they are still not widely implemented, partially due the challenges in maintaining the safe operation of such systems with a long-projected storage time. The presented study provided a Fault Tree (FT) analysis for the causal relationships of storage system failures with respect to the root causes, and further the potential integration of the FT with the dynamic Bayesian network (DBN) in order to monitor offshore salt caverns in the long-term operations. The presented study can incorporate both physics-of-failure models and sensory data to update the failure probabilities of the components in the offshore salt cavern storage system. The fault tree considers unintentional flow as the main failure mode and divides the system into three subsystems: the wellhead, the well and the cavern itself. Then, each individual subsystem is studied based on the components in the subsystem and the NORSOK standards for safe well design. The fault tree presented in this study also outlines the relationship between the failure modes and the effect they have on the system failure. Th fault tree analysis tool can then be converted into a DBN to provide a long-term monitoring framework for offshore CCS systems with limited observability, where real-time data can be utilized in order to update the probabilities of failure of components over time.

Foamed asphalt is the main means of achieving largescale road repairs. Small foam asphalt mixture mixers are necessary test equipment before the road construction process. To avoid serious injuries due to equipment failure, the structural reliability of the mixer is analyzed using failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Foamed asphalt is a metastable state product, and the mixer operation requires immediate and efficient operation, which results in a relatively complicated structure of the device. Potential faults are easily overlooked in reliability analyses of mixer systems. For FMEA, the familiarity proportionality coefficient should be introduced to ensure accurate ranking of the risk priority numbers and to reduce potential bias due to subjective evaluations by personnel. For FTA, the bottom event with the lowest probability of failure is removed while the bottom event with the smallest risk priority numbers and correlations are combined. The obtained FMEA table and structural fault tree model can improve the accuracy of the risk priority numbers, effectively reduce the workload of the reliability analysis, and obtain the structural reliability with less data.NomenclatureFoamed asphalt. Small foam asphalt mixture mixer. Risk priority number. Fault tree analysis. Failure mode and effects analysis. Reliability. The familiarity ratio coefficient of the scorer.

In view of the current study on the reliability analysis for complex new equipment systems, the fault tree analysis method is used in reliability engineering (Fault Tree Analysis, FTA). However, qualitative and quantitative analysis for complex equipment systems reliability are difficult problems. In this paper, firstly, fault tree analysis method is introduced, secondly, through an instance of a certain type of equipment, mathematical description and analysis methods for qualitative and quantitative analysis of system reliability are carried out. Finally, the results from fault tree analysis is presented and discussed, which shows that FTA is a method can quickly and accurately analyze equipment systems reliability, and is a designer tool for evaluation and improvement of system reliability.

The focus of this work is on the dependability analysis of safety or mission-critical systems; in particular, we concentrate on the control subsystem, which is made up of several components. We assume that the components, which may be designed with the support of hardware—software codesign tools, are characterized by dependability (e.g. failure rate) parameters, which may derive from simulators of the components while they are under development, or as a result of testing (possibly combined with fault injection techniques). By using combinatorial and state-space-based techniques it is possible to derive the reliability of the whole system as a function of the system configuration and of the component parameters values, and to identify the criticality of a given component or subset of components. The analysis is performed by applying Fault Tree Analysis (FTA) techniques enhanced with recently introduced features that allow one to remove the components’ independence assumptions imposed by classical FTA, and to include the possibility of component as well as subsystem repair.

In this paper, the authors demonstrate how fault tree analysis could be used to perform reliability analysis of hardware-software systems. The functional dependency of the hardware components on the interfacing software components is appropriately modeled using fault trees. The MIT (Massachusetts Institute of Technology)-CSR's (Center for Space Research) ACIS (Advanced X-ray Astrophysics Facility Imaging Charge Couple Device Imaging Spectrometer) system is used to illustrate the fault tree analysis method in reliability analysis of complex hardware-software systems. The ACIS science instrument system is a space borne system to acquire and process X-ray images over the sky, and sends them to earth. It has hardware and software components with interfaces between them making it a very good example of a complex hardware software system. This approach could be used in analyzing other complex systems being designed today and in identifying the critical components to make the system safe and more reliable.

This paper proposes a new goal-oriented (GO) method for reliability analysis of repairable systems with multiple-input and multi-function component (MIMFC). First, a new GO operator combination, which is composed of a new function GO operator and a new auxiliary GO operator, is created to represent MIMFC. The new function GO operator named as Type 22 operator is created to represent MIMFC itself, and the auxiliary GO operator named as Type 15B operator is created to represent multi-condition control signals of MIMFC. Then, GO operation formulas of the new GO operator combination are deduced based on logical relationships among inputs, outputs, and the component itself. The reliability analysis process of the new GO method is formulated. Furthermore, this new GO method is applied for the first time in steady availability analysis and qualitative analysis of the fan drive system of a power-shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis (FTA) and Monte Carlo simulation (MCS), and the comparison results show that this new GO method is reasonable and advantageous in reliability analysis of repairable systems with MIMFC. Moreover, the analysis process shows that it is more advantageous in the aspect of building system models and conducting reliability analysis. Overall, this paper not only improves the basic theory of the GO method and expands the application of the GO method, but it also provides a new approach for reliability analysis of repairable systems with MIMFC.