Model-driven safety assessment of robotic systems

This paper taking the sub fault tree of overload rope loosening device abnormal in the stacker crane system of the automatic storage and retrival warehouse as an example, combined with Gaussian fuzzy number theory and expert language judgment method, the unknown bottom event failure probability is calculated, and then the fault probability of the top event are calculated, and the quantitative analysis of fault tree is completed. The results show that the system fault tree can be analyzed quantitatively by using Gaussian fuzzy number in the absence of probability data of base event.

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.

This is the 17 slide PowerPoint presentation of Kondziolka, Briggs and Zhang's paper. Manufacturers worldwide are developing substitutes for refrigerants with high global warming potential (GWP) to address climate change concerns. Since 2007, we have used fault tree analysis (FTA) and Computational Fluid Dynamics (CFD) modeling to evaluate the safety of hydrofluoroolefin (HFO) refrigerants in the automotive and stationary sectors, an approach that has proven acceptable to several regulatory bodies. In this paper we describe the overall approach we have used, illustrated by several case examples involving HFO refrigerants (e.g., HFO-1234yf and HFO-1234ze(E)). This includes a description of open source CFD software to assess and visualize refrigerant dispersion. We also discuss the challenges of FTA parameterization when quantitative data are scarce, particularly as regards human factors. Finally, we share our experience using probabilistic tools for evaluating FTA uncertainty and comparative approaches for placing risks in context for the benefit of regulatory authorities.

Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability.

The Italian construction sector suffered more than the other industrial sector the negative effects of the world economic crisis. A consequence has been a change in its organization concerning labour and managent on the health and safety issues. Evidence of this can be found in an increase of fatal accidents and occupational diseases. In particular occupational diseases became a health concern in construction. The aim of the presented research work is to assess the hazard of asbestos fibres inhalation due to decommissing of old industrial promises with roofing of asbestos. The developed approach starts from a probabilistic assessment of the risk, using the Risk Analysis tools, the Fault Tree Analysis (FTA) and the Failure Mode and Effect Analysis (FMEA). Risk assessment through the FTA is based on a backward approach: it starts from the malfunction of the system, called Top Event, in order to look for the triggering causes (events); on the contrary, the FMEA analysis is based on a forward approach: they start from a set of events and go on in order to analyse what kind of malfunctioning these can generate in the system. Both the FTA and the FMEA are applied to three approved decontamination techniques (encapsulation, confinement and removal), and for each of them operational phases, equipment, building products and PPE (Personal Protective Equipment) are evaluated highlighting the procedures needed to operate in complete safety. With this approach the main factors that contribute to the increase of the risk of asbestos fibres inhalation are detected: preliminary analysis and assessment of the state of building decay, the use and maintence of PPE and machinery, and the supervision during main working phases.

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it clearer when one event affects a later one. Since this effect needs to be understood to construct an event tree correctly, we argue that the two notations should be used together. We then show how the Bayesian Network enables the factors that influence the outcome of events to be represented explicitly. In the case study, this allowed the train derailment model to be generalised and applied in more circumstances. Although the resulting model is no longer just an event tree, the familiar event tree notation remains useful.

We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.

PurposeThis paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability.Design/methodology/approachFor practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose.FindingsHazard decision tree (HDT) was configured to compute the hazards of each component and the whole AGV robot system. Through this research, a promising technical approach was established, allowing decision-makers to identify the critical components of AGVs along with their crucial hazard phases at the design stage.Originality/valueAs complex systems have become global and essential in today’s society, their reliable design and determination of their availability have turned into very important tasks for managers and engineers. Industrial robots are examples of these complex systems that are being increasingly used for intelligent transportation, production and distribution of materials in warehouses and automated production lines.

Fault Tree Analysis (FTA) is a method that directly focuses on the modes of failures. The FTA is a graphical representation of the major faults or critical failures associated with a product, as well as the causes for the faults and potential countermeasures. The aim of this research paper is to calculate the probability of the top event – the failure of the process using FTA and propose a technique to prioritize factors for action design and reduce the likelihood of a top event failure based on manufacturers' requirements. We have constructed a qualitative fault tree to produce office components packed and sealed in blister packs using a KOCH KBS-PL machine. We defined the top event G – the production of office components, packed and sealed in blister packs on the machinery KOCH KBS-PL. Then we defined events leading to top events down to individual failure factors. Based on the links between the fault tree and the probability of failure, we performed a quantitative analysis to determine the probability of failure of individual events. We found out that the probability of failure of G is 5.04%. Subsequently, we identified which factors most significantly reduce the resulting probability of failure of factor G. These are the factors: E – feed rate, F – cooling, AL – incorrect setting and D – break. It has been proven that by controlling these 4 factors, we can reduce the probability of failure of top event G to 2.36%, provided that effective measures are taken. The final proposal meets the requirements of several manufacturers for a fast, efficient, and cost-effective solution. We have created a proposal that saves time, has minimal software and hardware requirements, and is easy to use. The efficiency and effectiveness of the proposal was that we identified the weakest points in the fault tree that most significantly cause the top event to fail. This prioritized the factors for the design of the measures.

The fault tree of the pipeline was studied by investigating the failure modes and statistics of pipeline accidents of PetroChina. After a quantitative analysis of the impact factors for different failure modes, a Quantitative method for calculating the failure possibility of pipeline was established. The result was used to improve the most widespread pipeline risk assessment method - Kent scoring method. It can give comparable quantified description of the pipeline risk under current operation condition, and can give further support for decision-making of integrity management.

Fault Tree Analysis (FTA) is a powerful analytical technique for analyzing system reliability and safety by enumerating any possible safety-critical failure modes, which is very useful for identifying the risks and weaknesses in the system. Therefore, FTA is widely applied to the safety evaluation of large-scale and mission-critical systems. However, the following problems are usually pointed out when building a fault tree for a complex system: 1) System modeling is a hard and time consuming work, and 2) FTA models are difficult to be validated. In this paper, we propose a new method for system reliability analysis based on Multilevel Flow Models (MFM) and Goal Tree-Success Tree (GTST) methods. We use Goal Tree (GT) methodology to model the target system at a higher and system level, and use the Success Tree (ST) together with MFM at a lower and functional level. In this way, modeling effort could be significantly reduced. In this paper, an algorithm is also presented to translate the GTST-MFM model into ST model based on which qualitative reliability analysis can be performed by the Fusell-vesely algorithm. In this paper, a Low Head Safety Injection System (LHSIS) is taken as a case study to exemplify how to apply our proposed GTST-MFM method to model the system and to validate fault trees directly built by deductive method.

This chapter provides an introduction to the steps involved in creating dependable systems. This starts with a description of functional hazard assessment (FHA). The steps involved in preliminary system safety assessment (PSSA) and system safety assessment (SSA) are reviewed. The chapter introduces fault tree analysis (FTA) and failure modes and effects analysis (FMEA) as important tools in the safety assessment process. This chapter also introduces the basics of probability theory which can guide quantitative assessment. The concepts behind common cause analysis are introduced. To make the book self-contained, more detailed mathematical concepts are presented in the appendices which can be skipped by less mathematically inclined readers.

The performance of manufacturing systems or equipment is, to a great extent, dependent upon the condition of their components. Closely monitoring the condition of the critical components and carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce system downtime and improve overall productivity. Fault tree analysis (FTA) is a powerful tool for reliability studies and risk assessment. However, most research on FTA focuses on the generation of minimum cut sets and how to calculate the probability of main events. As a result, the issue concerning the ordering of basic events in a fault tree has been largely neglected. In this paper, a novel approach based on rough set theory and a pairwise comparison table for fault diagnosis is proposed. The approach attempts to learn from the pattern of decision-making by domain experts from past experience and uses the knowledge acquired, which is in the form of a minimum decision rule set, to determine the ordering of basic events in a fault tree. The details of the approach, together with the basic concepts of rough set theory, are presented. A case study is used to illustrate the application of the proposed approach. Results show that a reasonable ordering of basic events in a fault tree can be generated easily. With the ordering of basic events determined, a maintenance engineer in a manufacturing plant can then carry out fault diagnosis in an efficient and orderly manner.

The causes of biosensor signal-to-noise ratio decrease have been investigated by means of fault tree analysis (FTA) relying on fuzzy reasoning to account for uncertainty. Using the fuzzy FTA methodology proposed herein, all ultimate causes or combination of causes, attributed to the device components, the interaction of the device components or the human factor, that are responsible for or contribute to the top event, have been recognized and quantified based on 1) measurements for the deterministic contributors and 2) experience for the stochastic contributors. The tree structure has been designed by combining deduction and induction, top-down and bottom-up techniques, thus establishing a dialectic tradeoff which brings this method closer to scientific logic, permitting the introduction of deeper knowledge into the surface or experiential knowledge level characterizing FTA. The methodology has been implemented in the case of bilayer lipid membrane biosensors and has proven to be an efficient tool for internal diagnostics and fault compensation. The partitioning of the space of the variables and the determination of the fuzzy rules serving as the inference engine for diagnosis have been achieved by using experts' opinion through a modified four-stage Delphi method. An extended case example of the diagnostic operation is presented, exploiting the causes of "Loss of Response" of a biological oxygen demand (BOD) biosensor measuring under working conditions, proving the effectiveness of this method, which can minimize research and development expenditures for corrective action, i.e., increase of biosensor tolerability degree.

SARISTU morphing wing is mainly based on three devices: enhanced adaptive droop nose (EADN), adaptive trailing edge device (ATED) and winglet active trailing edge (WATE). All these devices are used together to improve the overall wing efficiency and to reduce the aerodynamic noise. The safety activities described in this paper were performed to verify whether this concept can comply with the standard civil flight safety regulations and airworthiness requirements. The safety analysis was performed in two steps: a functional hazard assessment (FHA) and a system safety assessment (SSA). Both analyses were performed at wing integration level (IS12) and at single morphing wing devices level. A complete mapping between these two levels of analysis was structured from the beginning of the process, starting from the aircraft functional definition, to integrate and harmonize both FHA and fault trees results. FHA was used to assess the severity of the identified Failure Conditions and then allocate safety requirements. Fault tree modelling technique was used to verify the compliance of the system architectures to the quantitative safety requirements resulting from the FHAs. The paper sets out the hypotheses and common data used by the fault trees. A complete but simple example illustrates the safety approach all through the different steps of the safety methodology. Other safety activities commonly performed in the aeronautical field such as the particular risk analysis (PRA), common mode analysis (CMA) and zonal safety analysis (ZSA) were identified in the frame of SARISTU project. This paper concludes with a summary highlighting the main results of these safety activities with some lessons learned from the safety approach adapted to SARISTU context.