Introducing ASIL inspired dynamic tactical safety decision framework for automated vehicles

Existing automotive Hazard Analysis and Risk Assessment (HARA) process as discussed by the international standard ISO 26262 is static in nature. While the standard describes a systematic process to incorporate functional safety in the development process of Electrical & Electronic (E/E) systems, it fails to address the needs of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) systems. In order to ensure the safety of ADAS and AD systems, it is important to incorporate the changing nature of interactions between the system and the environment, in the safety analysis process for ADAS and AD systems. In this paper, the authors argue the need for a dynamic approach for automotive safety analysis by adapting the tactical safety for ADAS and AD systems depending on the real-time operational capability and real-time ASIL (Automotive Safety Integrity Level) rating of a situation, and discuss a framework for this process. The novelty and therefore contribution of this paper lies in the proposed ASIL inspired dynamic tactical safety framework, which evaluates the severity, controllability and exposure ratings in real-time based on the real time values of the various vehicle and environment parameters. These ratings are used to assign a real-time ASIL value which is used to determine the tactical decisions in order to lower the ASIL value in real-time by altering the functional (operational) capability of the system. Furthermore, the framework is explained with the help of a case study based on a combined Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB) system

Buffet test in the National Transonic Facility

A buffet test of a commercial transport model was accomplished in the National Transonic Facility at the NASA Langley Research Center. This aeroelastic test was unprecedented for this wind tunnel and posed a high risk for the facility. Presented here are the test results from a structural dynamics and aeroelastic response point of view. The activities required for the safety analysis and risk assessment are described. The test was conducted in the same manner as a flutter test and employed on-board dynamic instrumentation, real time dynamic data monitoring, and automatic and manual tunnel interlock systems for protecting the model

A spatio-temporal modelling and analysis of digital sensor data for underground mine health and safety

A Research Report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements of the degree of Master of Science 2017Health and safety of employees within their work environment is critical. In the mining industry and especially in underground mines, monitoring and management of health and safety of employees is particularly important Most underground mines today are not fully mechanized, except for coal mines. The industry thus still relies on and employs human personnel. Monitoring and managing these mines and hence personnel health and safety as they undertake their trade is therefore a necessity. Implementation of technology, especially in digital sensor systems and real-time spatial analysis systems, provides a means by which health and safety risk factors can be monitored and information gathered to facilitate determination of prevailing risks or prediction of such risks. Technology therefore can be used to make better decisions and implement specialized emergency response to avert or reduce the extent of injuries, casualties and damages in an underground mine. This research project looks into determination of prominent risk factors in an underground mine, determination of parameters for modeling of such risk factors and the implementation of ESRI’s ArcGIS platform for the retrieval and analysis of streaming sensor data about this parameter from an underground mine. A proof of concept (POC) system is developed that analyses streaming digital sensor data and determines the status of the underground mine environment. The results from this analysis are displayed in a dashboard application for a control room environment. The results and achievements of this research project, especially from a dashboard system perspective, show the possibilities of an integrated GIS-based solution for real-time data processing and determination of the prevailing conditions in an underground mine. This solution also opens up a wide pool of possibilities through which systems integration and its benefits can be achieved, especially in underground mines and focusing on health and safety, as previously silo systems can be integrated at data levels, enabling data sharing, analysis, predictions and making of informed decisions.MT201

Software timing analysis for complex hardware with survivability and risk analysis

The increasing automation of safety-critical real-time systems, such as those in cars and planes, leads, to more complex and performance-demanding on-board software and the subsequent adoption of multicores and accelerators. This causes software's execution time dispersion to increase due to variable-latency resources such as caches, NoCs, advanced memory controllers and the like. Statistical analysis has been proposed to model the Worst-Case Execution Time (WCET) of software running such complex systems by providing reliable probabilistic WCET (pWCET) estimates. However, statistical models used so far, which are based on risk analysis, are overly pessimistic by construction. In this paper we prove that statistical survivability and risk analyses are equivalent in terms of tail analysis and, building upon survivability analysis theory, we show that Weibull tail models can be used to estimate pWCET distributions reliably and tightly. In particular, our methodology proves the correctness-by-construction of the approach, and our evaluation provides evidence about the tightness of the pWCET estimates obtained, which allow decreasing them reliably by 40% for a railway case study w.r.t. state-of-the-art exponential tails.This work is a collaboration between Argonne National Laboratory and the Barcelona Supercomputing Center within the Joint Laboratory for Extreme-Scale Computing. This research is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC02- 06CH11357, program manager Laura Biven, and by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Dynamic risk assessment of process operations

Process engineering systems have become increasingly complex and more vulnerable to potential accidents. The risks posed by these systems are alarming and worrisome. The operation of these complex process engineering systems requires a high level of understanding both from the operational as well as the safety perspective. This study focuses on dynamic risk assessment and management of complex process engineering systems’ operations. To reduce risk posed by process systems, there is a need to develop process accident models capable of capturing system dynamics in real-time. This thesis presents a set of predictive process accident models developed over four years. It is prepared in manuscript style and consists of nine chapters, five of which are published in peer reviewed journals. A dynamic operational risk management tool for process systems is developed, considering evolving process conditions. The obvious advantage of the developed methodologies is that it dynamically captures the real time changes occurring in the process operations. The real time risk profile provided by the methodologies developed serve as performance indicator for operational decision making. The research has made contributions on the following topics: (a) process accident model considering dependency among contributory factors, (b) dynamic safety analysis of process systems using a nonlinear and non-sequential accident model, (c) dynamic failure analysis of process systems using principal component analysis and a Bayesian network, (d) dynamic failure analysis of process systems using a neural network and (e) an integrated approach for dynamic economic risk assessment of process systems

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance

Developing a Framework for Dynamic Risk Assessment Using Bayesian Networks and Reliability Data

PresentationProcess Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis, LOPA, etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis. ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O’Connor Process Safety Center – Qatar (MKOPSC-Q) to develop a methodology that establishes a framework for a tool that monitors in real time the potential increases in risk levels as a result of pre-identified risk factors that would include the use of KPIs (leading or lagging) as observations or evidence using Bayesian Belief Networks (BN). In this context, the paper presents a case study of quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BBN, modification of the skeletal BN to incorporate KPIs (loss of primary containment (LOPC), equipment, management and human related), and testing of the BBN with forward and backward inferences. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made