Safety Analysis Approach Research Articles

City-Level China Traffic Safety Analysis via Multi-Output and Clustering-Based Regression Models

In the field of macro-level safety studies, road traffic safety is significantly related to socioeconomic factors, such as population, number of vehicles, and Gross Domestic Product (GDP). Due to different levels of economic and urbanization, the influence of the predictive factors on traffic safety measurements can differ between cities (or regions). However, such region-level or city-level heterogeneities have not been adequately concerned in previous studies. The objective of this paper is to adopt a novel approach for traffic safety analysis with a dataset containing multiple target variables and samples from different subpopulations. Based on a dataset with annual traffic safety and socioeconomic measurements from 36 major cities in China, we estimate single-output regression models, multi-output regression models, and clustering-based regression models. The results indicate that the 36 cities can be clustered into a metropolitan city class and a non-metropolitan city class, and the class-specified models can notably improve the goodness-of-fit and the interpretability of city-level heterogeneities. Specifically, we note that the effect of primary and secondary industrial GDP on traffic safety is opposite to that of tertiary industrial GDP in the metropolitan city class, while the effects of the two decomposed GDP on traffic safety are consistent in the non-metropolitan city class. We also note that the population has a positive effect on the number of fatalities and the number of injures in metropolitan cities but has no significant influence on traffic safety in non-metropolitan cities.

Redesign the layout of agro-industry by using occupational safety and health analysis at tempe chips SME

Layout is an important factor of agro-industry to improve productivity and reduce potential risk of work accident. The objective of this research was to redesign the layout of Tempe Chips Small and Medium Enterprise (SME). Bu Nurjanah Tempe Chips SME Malang, East Java was selected as research location because of poor lay out system and it has same character with Tempe Chips SME that had fire accident. This research used Occupational Health and Safety Analysis and Systematic Layout Planning (SLP) approach in designing agro-industry layout that consist of activity relationship chart, activity relationship diagram, and area needs. The results of the research showed that Tempe Chips SME has potential fire hazards and food contamination. Regarding the Occupational Safety and Health standard, this research advice re-layout for several production facilities such as access to drinking water, toilets and washing facilities, and clean cafeteria to improve effectiveness of production.

Model-Based Safety Analysis for the Fly-by-Wire System by Using Monte Carlo Simulation

Safety analysis is one of the important means to show compliance with airworthiness requirements. The traditional safety analysis methods are significantly dependent on analysts’ skills and experiences. A model-based safety analysis approach is proposed for typical fly-by-wire (FBW) systems based on the system development model built via Simulink, by which the response of system performances can be simulated. The safety requirements of the FBW system are defined by presenting the thresholds of system performance metrics, and the effects of failure conditions on aircraft safety are determined according to the system response simulation by injecting failures or failure combinations into the Simulink model. The Monte Carlo simulation method is used to calculate the probability of unsafe conditions, whose effects are determined by the system response simulation with fault injections. Finally, a case study is used to illustrate the effectiveness and advantages of our proposed approach.

Evaluating pedestrian vehicle interaction dynamics at un-signalized intersections: A proactive approach for safety analysis

The present research demonstrates the use of advanced trajectory based data to analyze road user interactions at an un-signalized intersection under heterogeneous traffic complexities. This study demonstrates an improvement over the conventional grid-based analysis to estimate surrogate safety measures (SSM). An advanced pattern-based approach to categorize pedestrian-vehicle interactions based on the road user behavior is proposed in the study. A concept of a two-interaction pattern has been applied, which deals with the responsive and non -responsive behavior of the road users, respectively. The behavior-based patterns were categorized based on the SSM like Speed, Time to Collision, and Gap Time profiles of the pedestrian and vehicle interacting on an un-signalized intersection. On conducting a variable importance test, i.e., k-fold test, it was comprehended that, for pattern-1, Time to collision (TTC), and for pattern-2 both TTC and Post Encroachment Time (PET) were showing required importance. Further, Import Vector Machine (IVM) approach was used to classify the severity levels based on selected indicators computed from 1486 events, occurring at three Un-Signalized intersections in India. The proposed severity levels will help to test and evaluate various infrastructure and control improvements for making urban intersections safe for road users. It was observed from the severity levels of both the patterns that, events involving non-evasive behavior can also result in critical interaction. Overall, the research provides an advanced framework for evaluating and improving the safety of the uncontrolled intersections.

Integrated Approach of Safety, Sustainability, Reliability, and Resilience Analysis via a Return on Investment Metric

The process design and technology selection are driven by multiple objectives including technical, economic, environmental, safety, and resilience drivers. Traditionally, techno-economic analyses h...

A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology

Abstract Chemical process industries (CPI) are inherently hazardous complex systems where large inventory of extremely flammable and explosive chemicals are processed and stored in a highly congested process area. A reliable safety analysis method plays a significant role to measure risks and to develop preventive strategies in process industries. This paper proposed a novel Fuzzy Bayesian Network for dynamic safety analysis of process systems by incorporating Bayesian network (BN) with Fuzzy Best Worst Method (Fuzzy-BWM). In the proposed approach a comprehensive and in-depth analysis of human and organizational factors (HOFs) involving in the accident scenario occurrence was also provided by integrating Human Factor Analysis and Classification System (HFACS) and System Hazard Identification, Prediction and Prevention (SHIPP) methodology into the model. An ethylene storage tank was selected to verify the applicability of the proposed approach and its application potential. The study also explained a comparison between the results of the proposed Fuzzy-BWM approach with the conventional BN approach and a quantitative risk assessment (QRA) conventional technique such as bow-tie (BT). The findings revealed the capability of the proposed Fuzzy-BWM approach to provide high reliable results and to detect risks that using the BT and BN approaches were not identified.

Safety analysis of integrated adaptive cruise and lane keeping control using multi-modal port-Hamiltonian systems

A modern vehicle can be viewed as a complex cyber–physical system (CPS) where the vehicle dynamics interact with the software control systems. Adaptive cruise control (ACC) and lane keeping control (LKC), in particular, are foundational features for semi-autonomous and autonomous driving. Safety analysis of such systems is extremely important for realizing vehicle autonomy. Ensuring safety in such complex CPS is very challenging, especially in the presence of interactions between multiple subsystems, nonlinearities, hybrid dynamics, and disturbances. This paper presents an approach for safety analysis of automotive control systems using multi-modal port-Hamiltonian systems. The approach uses the Hamiltonian function as a barrier between the energy levels of the safe and unsafe states and employs passivity to prove that trajectories cannot cross this barrier. The approach is applied to the safety analysis of a vehicle dynamics composed with ACC and LKC. The goal is to ensure that the host vehicle will not collide with a lead vehicle and will not skid off of the road. The control design is implemented and evaluated using a hardware-in-the-loop simulation platform. The experimental results demonstrate the safety analysis approach including the impact of implementation effects such as discretization and quantization.

A novel extension of DEMATEL approach for probabilistic safety analysis in process systems

Quantitative risk assessment (QRA) techniques methodically assess the likelihood, impact, and subsequently the risk of adverse events. In a typical QRA method, one of the main intentions is to identify the critical root events which mostly contribute to the risk of the top event (TE) and requiring subsequent corrective actions (CAs). Finding the critical events which contribute to the risk is significantly dependent on the assumptions and methods applied to integrate the probabilities of different events and these events may have really low probability of occurrence. Thus, the probability reduction of the critical root events and subsequently the system’s failure does not lead to an optimal solution. For this reason, ranking the CAs without consideration of several aspects such as their influence on root events probability, inter-relationships, and direct/indirect cost, is not an appropriate approach. This study aims to introduce an approach to deal with the aforementioned situations. A novel extension to DEMATEL (decision making trial and evaluation laboratory) named Pythagorean fuzzy DEMATEL is proposed on a common probabilistic safety analysis. As a case study, a collapse in an offshore facility platform (including 42 basic events and corresponding 30 CAs) is considered to illustrate the effectiveness of the presented approach. The application of the model confirms its robustness in prioritizing critical root events and CAs compared with a conventional model, consideration of the influencing factors, and a dynamic and flexible structure.

(Invited) A Combined Calorimetry and Modeling Approach for Safety Analysis of Large-Size Cells

Safety is a key aspect of battery energy storage systems. Understanding safety aspects of large-size cells is complicated not only by the interplay of multiple physical phenomena but primarily due to limitations in experimental capabilities that can accurately measure all the relevant signals. Due to the limits of battery testing, information such as temperature can be only measured in a practical way. Recognizing this gap, a combined calorimetry and modeling approach is developed for large-size cells to shorten design cycles and reduce cost to optimize batteries with improved safety. Safety of large-size cells is complementarily determined by safety features implemented at multi-levels including material, chemistry, cell and system design. Calorimetry methods have been widely adopted to analyze battery failure mechanisms and hazard under electrical, mechanical and thermal abuse conditions at chemistry and cell levels [1][2]. It prefers sample spatially uniform responses to abuse conditions and is lack of capability to discern orderly sequences to characterize failure evolution within a cell and failure propagation among cells. In this presentation, an integrated multiphysics li-ion battery safety model built on a nonlinear multi-scale multi-domain (MSMD) model framework [3] is introduced. Modularized hierarchical architecture of the MSMD framework allows independent choice of submodels. In the combined approach, the models are adept at utilizing experimental results. calorimetry results serve as guideline to tune the integrated multiphysics model, such as determining physical behaviors being simulated in the submodels and identifying corresponding modeling parameters. Modeling results are then correlated with experimental data to identify potential experimental uncertainties. Safety performance at higher levels can be directly estimated using this model without additional model calibration. The combined approach enables battery safety being addressed efficiently across multiple cells at real-world scenario. It provides insights on functionalities of safety mechanisms and its contribution to overall battery safety. The approach has been applied to study transient gas flow behaviors within an 18650 battery cell subject to internal short circuit and validate safety technical assumptions for the design of a novel packaging architecture for lithium-ion batteries [4]. Moreover, this approach can quantify non-measurable properties and its effects on battery failure severity, such as thermal interfacial properties among large-size cells. Acknowledgement This study was supported by Computer Aided Engineering for Batteries (CAEBAT) project of the Vehicle Technologies Office, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy, located at the National Renewable Energy Laboratory. Reference [1] C. Lampe-Onnerud, et al., Safety Studies on Lithium-Ion Batteries by Accelerating Rate Calorimetry, Battery Conference on Applications and Advances, 1999 [2] W. Walker, et al., Statistical characterization of 18650-format lithium-ion cell thermal runaway energy distributions, NASA Aerospace Battery Workshop, Huntsville, Alabama, 2017 [3] G.-H. Kim, K. Smith, K.-J. Lee, S. Santhanagopalan, and A. Pesaran, J. Electrochem. Soc., 58, 8 (2011) [4] Cadenza Innovation, INC (2018). ARPA-E Final Technical Report, Novel low cost and safe Lithium-Ion Electric Vehicle Battery, DE-AR0000392

An evidence-based approach to health and safety management in megaprojects

With regard to the technical challenge on health and safety in megaprojects, this article puts forward a new evidence-based health and safety analysis (EHSA) approach in megaprojects. The purpose of the article is to initiate a conceptual EHSA framework to support evidence-based learning in practice-oriented research into large-scale complex health and safety management in megaprojects. The EHSA approach aims to find an innovative way to facilitate the collection of data and information from accumulated professional knowledge about accidents and failures as well as good practices and innovations, to derive useful lessons to inform improved practice in health and safety management in megaprojects. Through a scenario-based case study, this article will demonstrate how EHSA approach can effectively support health and safety management in megaprojects. It is expected that this article can make a good contribution to the body of knowledge by providing the new EHSA approach and a practice-oriented experimental case study on the use of EHSA approach for health and safety management in construction projects so as to inform future practice and research at strategic and tactic level.

Safety analysis of offshore decommissioning operation through Bayesian network

ABSTRACTDecommissioning of offshore platforms is becoming increasingly popular. The removal of these heavy steel structures is characterised by high risks that may compromise personnel safety and loss of assets. The removal operation relies on dedicated barges and heavy lift vessels that may descent or capsize because of mechanical or structural failure. The knowledge of associated hazards is driven by experience and failure data are often obtained empirically through analogous operations, which further introduces uncertainty to the risk analysis. This paper proposes an integrated safety analysis approach for conducting a decommissioning risk analysis of offshore installations. The approach incorporates hierarchical Bayesian analysis (HBA) with Bayesian network (BN) to assess the accident causations leading to futile decommissioning operation. First, the overall system failure of a lifting vessel was reviewed with an emphasis on where safety issues arise. In addition, the failure data obtained from expert judgements were aggregated through statistical distribution based on HBA. The aggregated failure data are then used to conduct dynamic safety analysis using BN, to assess and evaluate the risks of offshore jacket removal operations. The accident model is illustrated with a case study from Brent Alpha decommissioning technical document to demonstrate the capability of incorporating HBA with BN to conduct a risk analysis.

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

As safety-critical systems, grid cyber-physical systems (GCPSs) are required to ensure the safety of power-related systems. However, in many cases, GCPSs may be subject to uncertain and nondeterministic environmental hazards, as well as the variable quality of devices. They can cause failures and hazards in the whole system and may jeopardize system safety. Thus, it necessitates safety analysis for system safety assurance. This paper proposes an architecture-level safety analysis approach for GCPSs applying the probabilistic model-checking of stochastic games. GCPSs are modeled using Architecture Analysis and Design Language (AADL). Random errors and failures of a GCPS and nondeterministic environment behaviors are explicitly described with AADL annexes. A GCPS AADL model including the environment can be regarded as a game. To transform AADL models to stochastic multi-player games (SMGs) models, model transformation rules are proposed and the completeness and consistency of rules are proved. Property formulae are formulated for formal verification of GCPS SMG models, so that occurrence probabilities of failed states and hazards can be obtained for system-level safety analysis. Finally, a modified IEEE 9-bus system with grid elements that are power management systems is modeled and analyzed using the proposed approach.

Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions Over the Internet

In the last few years, unmanned aerial vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over the Internet (Internet of drones (IoD)). Two approaches will be used in performing the safety analysis: (1) a qualitative safety analysis approach and (2) a quantitative safety analysis approach. The first approach uses the international safety standards, namely, ISO 12100 and ISO 13849 to assess the safety of drone’s missions by focusing on qualitative assessment techniques. The methodology starts with hazard identification, risk assessment, risk mitigation, and finally draws the safety recommendations associated with a drone delivery use case. The second approach presents a method for the quantitative safety assessment using Bayesian networks (BN) for probabilistic modeling. BN utilizes the information provided by the first approach to model the safety risks related to UAVs’ flights. An illustrative UAV crash scenario is presented as a case study, followed by scenario analysis, to demonstrate the applicability of the proposed approach. These two analyses, qualitative and quantitative, enable all involved stakeholders to detect, explore, and address the risks of UAV flights, which will help the industry to better manage the safety concerns of UAVs.

Exploring Spatial Relationship between Roadway Safety and Wet Condition Risk Factors Based on Systemic Safety Analysis Approach

AbstractThis study applies spatial analysis to investigate the relationship between crashes, wet road conditions, and physical roadway variables. Wet roadway and flood risk factors are not included...

Design evaluation of large-scale sodium integral effect test facility (STELLA-2) using MARS-LMR

The STELLA program is one of the key activities to support the development of PGSFR in Korea and the target is the DHRS. The STELLA-2 facility is an integral effect test facility and scaled-down components of PGSFR are included. For the STELLA-2 design evaluation, a representative DBE (LOF with LOOP) was selected for the MARS-LMR analysis with the same assumption and same approach of PGSFR safety analysis. The method and results of the transient analysis are described as well as the comparison with the PGSFR. The general trend was similar and showed reasonable results, but some differences were observed. However, the issues caused by these differences were found to be managed by adjusting the experiment scope and changing the experiment method. Furthermore, the validity of STELLA-2 was confirmed through collaboration with safety analysis group of PGSFR. The results of this work will be used as a base material for experiment condition setting and scope decision. Also, the valuable understanding of the facility’s characteristics will come out through the virtual experiment using MARS-LMR.

Approaches for Safety Analysis of Gas-Pipeline Functionality in Terms of Failure Occurrence: A Case Study

The development of appropriate assessment methods of gas-pipeline functionality contributes to the reduction of failure consequences and helps engineers to make the right decisions as to the optimal solution choice for technical facilities, as well as provides procedures to protect their users and the surrounding environment. This paper presents methods for the assessment of gas network operation. Pipe failure data were collected from a gas distribution network. A statistical analysis of the failure of gas networks was made. An attempt was made to isolate seasonal and accidental fluctuations in the tested failure stream. The Poisson distribution was proposed as a model of failure distribution of gas networks. The conducted analysis allowed us to propose the forecasting method of acceptable failure consequences using the homogeneous Markov chain. The obtained results are valuable for supporting the management of urban gas networks, mainly in terms of the strategic modernization plans and the rehabilitation techniques.

Systemic approaches to incident analysis in aviation: Comparison of STAMP, agent-based modelling and institutions

The rapid development and increasing complexity of modern socio-technical systems suggest an urgent need for systemic safety analysis approaches because traditional linear models cannot cope with this complexity. In the aviation safety literature, among systemic accident and incident analysis methods, Systems Theoretic Accident Modelling and Processes (STAMP) and Agent-based modelling (ABM) are the most cited ones. STAMP is a qualitative analysis approach known for its thoroughness and comprehensiveness. Computational ABM approach is a formal quantitative method which proved to be suitable for modelling complex flexible systems. In addition, from a legal point of view, formal systemic institutional modelling potentially provides an interesting contribution to accident and incident analysis. The current work compares three systemic modelling approaches: STAMP, ABM and institutional modelling applied to a case study in an aviation domain.

Formal model-based quantitative safety analysis using timed Coloured Petri Nets

Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) are by far the most frequently used qualitative and quantitative approaches in system reliability and safety analysis such as in the railway domain. FTA and ETA explain the causalities and consequences of hazards or accidents (e.g., rail traffic accidents) in terms of linear event sequences, which are difficult to incorporate none-linear relationships such as feedback. For quantitative analysis, FTA and ETA have disadvantages in dealing with dependent failure events. The quality assurance for fault trees and events trees is mainly carried out by peer review. In addition, traditional FTA and ETA are usually applied to systems that consists of non-repairable components. For systems that comprise repairable components, Markov models are widely used, which suffer however intensively from the state space explosion.Considering all these issues, we propose a formal model-based approach for quantitative safety analysis using timed Coloured Petri Nets (CPNs). There are three main contributions in this paper: firstly, a modelling method based on the specifications of timed message sequence charts, systems theory and decision tables for system components is raised for establishing timed hierarchical CPN models of systems that are appropriate for quantitative safety analysis. Secondly, state-space-based methods by exploring standard state space reports, and applying standard as well as non-standard queries to state spaces are presented to verify the untimed CPN models. Finally, methods of evaluating the safety characteristics of mean time to hazardous event and the probability of keeping in normal and safe states on the basis of the data collected during the simulation of the timed CPN models are provided. To illustrate our approach, a case study of a railway level crossing control system is presented as a running example throughout the paper.

Quantitative and qualitative safety analysis of a hemodialysis machine with S#

AbstractThis paper reports on our experiences of applying S# (“safety sharp”) to model and analyze the case study “hemodialysis machine.” The S# safety analysis approach focuses on the question, what happens if we place a controller with correct software into an unreliable environment. To answer that question, the S# toolchain natively supports the Deductive Cause Consequence Analysis, a fully automatic model checking‐based safety analysis technique that determines all sets of component faults with the potential of causing a system hazard. Furthermore, S# can give an approximate estimate of the hazard's probability. To demonstrate our approach, we created a model with a simplified controller of the hemodialysis machine and relevant parts of its environment and performed a safety analysis using Deductive Cause Consequence Analysis.

Fracture Analyses of Cracked Delta Eye Plates in Ship Towing

Based on fracture mechanics, a safety analysis approach is proposed for cracked delta eye plates in ship towing. The static analysis model is presented when the delta eye plate is in service, and the fracture criterion is introduced on basis of stress intensity factor, which is estimated with domain integral method. Subsequently, three-dimensional finite element analyses are carried out to obtain the effective stress intensity factors, and a case is studied to demonstrate the reasonability of the approach. The results show that the classical strength theory is not applicable to evaluate the cracked plate while fracture mechanics can solve the problem very well, and the load level, which a delta eye plate can carry on, decreases evidently when it is damaged.