229 publications from this institution
Autonomous transportation is an increasingly popular concept and is gradually becoming a reality. This transformation also changes the way people travel. For example, the autonomous ferry is an emerging alternative for residents living in coastal areas. To evaluate the safety of an autonomous ferry, a thorough safety review is necessary. This paper makes an initial attempt by developing a model for performing a risk assessment of collisions between an autonomous ship with manned vessels and applying this to a specific ferry operating in a canal. The safety barriers to prevent a collision are identified, as well as the respective failure modes. A Bayesian belief network is employed to model the collision and to quantitively assess the collision risk of the autonomous ferry. Relevant data are collected to perform a quantitative risk analysis. By running the model, the likelihood of a collision is calculated. A sensitivity analysis is also performed to identify the most contributing causes.
This chapter is intended to assist in the data collectionData collection and development of online risk assessmentOnline risk assessment for automated marine and maritime systemsMarine and maritime systems. Section 3.1 presents the data flowData flow in online risk assessmentOnline risk assessment modelsModel. One of the main steps, in the modeling process, is data collectionData collection and preparationData preparation, which is explained in Sect. 3.2. Lastly, in Sect. 3.3, the quantification processQuantification process of failureFailure probabilitiesProbability/frequencies using the collected data is presented and discussed. In this chapter, a dynamic positioningDynamic positioning (DP) system as an example of an automated complex systemComplex systems is presented to illustrate the process of data collectionData collection and modeling more clearly.
Failures in critical infrastructures can cause major damage to society. Wide-area interruptions (blackouts) in the electricity supply system have severe impacts on societal critical functions and other critical infrastructures, but there is no agreed-upon framework on how to analyze and predict the reliability of electricity supply. Thus, there is a need for an approach to cross-sector risk analyses, which facilitates risk analysis of outages in the electricity supply system and enables investigation of cascading failures and consequences in other infrastructures. This paper presents such an approach, which includes contingency analysis (power flow) and reliability analysis of power systems, as well as use of a cascade diagram for investigating interdependencies. A case study was carried out together with the Emergency Preparedness Group in the city of Oslo, Norway and the network company Hafslund Nett. The case study results highlight the need for cross-sector analyses by showing that the total estimated societal costs are substantially higher when cascading effects and consequences to other infrastructures are taken into account compared to only considering the costs of electricity interruptions as seen by the network company. The approach is a promising starting point for cross-sector risk analysis of electricity supply interruptions and consequences for dependent infrastructures.
No abstract is provided for this article.
The Arctic is a vast area with many future economic possibilities for the oil and gas, shipping and the fishing industries. The climate is harsh, the environment vulnerable, but the potential profits from future expansion in the area are huge. The on-going public debate on the Arctic includes discussions both for and against industrial development in these areas, however the reality is that as resources become scarcer in other parts of the world, Arctic expansion will become inevitable. Therefore adequate preplanning of the activities, understanding of the operational environment and development of barriers against undesired events becomes infinitely more important for sustainable, reliable and safe operation in the future. The fishing fleet has been operating in the Arctic region for decades and while the safety for the fishers is questionable, it is a matter of resources that drove and will continue to drive this expansion. The IMO’s Polar Code for shipping is now under construction and the fishing fleet will have to comply with this and other regulations for future operations in the Arctic. This paper focuses on the maintenance and safety management regimes and requirements of the fishing fleet currently operating in the Arctic. With long distances to service and help, and a short operating season, a reliable system is a mandatory requirement for the economic stability of these operations. Mutual benefits may be gained if operational experiences from fishing can be utilized by the oil and gas industry and ship transport when moving their operations into the arctic areas, whereas the fishing fleet can improve their safety performance through closer alignment with those standardized procedures applied in other industries.
<p>This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation. </p> <p>A case study of a tethered industrial inspection drone is considered. A system theoretic process analysis (STPA) is performed to identify how the system can fail. A Dynamic Decision Network (DDN), used as an online risk model, is built based on the results of the STPA. An optimization approach is used to choose an optimal parameter configuration that ensures an acceptable risk level.</p> <p>Through experimental tests, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it chooses values for the maximum speed, safety distance, and maximum vertical acceleration that produces an acceptable risk level. The parameters are updated during flight based on the output from the supervisory risk controller. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator.</p> <p>Video of the experimental results can be found at https://youtu.be/RKhG9bguRJY</p>
One challenge that has received attention in maritime industry is assessing the risk level of dynamic positioning (DP) systems in emergency situations. Statistics from recent years have shown that the risk level of some DP operations is above the industry's risk criteria. Operators have a significant impact on incidents' consequences by making responsive decisions. In emergencies, one is afforded little time to make a decision. Available risk models are not efficient enough to provide systems' risk level in a short period of time. In this study, the application of a new supervised methodology to assist decision making in emergencies is proposed. This method significantly reduces the processing and execution time of a system's probabilistic risk assessment models. In this methodology, the most probable failure scenarios are generated using an optimization model. The objective of the optimization model in this study is to find scenarios with the highest occurrence probabilities. The constraints are a system's dynamic simulation and its risk model. The proposed method is applied to three incidents that occurred in the Norwegian offshore sector in previous years. The results show that the model can predict the most probable scenarios with an acceptable accuracy in a very short time.
The Norwegian aquaculture industry has the potential to become the country’s leading ocean industry in the future. More than 99% of the produced biomass is Atlantic salmon and trout. Norwegian fish farming is characterised by operations that are susceptible to changing weather, wind and currents, and face challenges in terms of safety for fish, personnel, environment and material assets. Previous research and accident analyses reveal an incomplete knowledge of risk factors during aquaculture operations. In order to raise standards of safety in the workplace, operators need to be aware of the challenges to safety in their work environment. The objective of this paper is to describe and discuss the current status of the implementation of risk assessments in the Norwegian aquaculture industry, according to Norwegian legislation and compared with recommended requirements in the Norwegian standard for risk assessments (NS 5814). This standard largely follows ISO 31000 for risk management. We also propose, test and evaluate an improved approach to risk assessment that will ensure stronger operator involvement. Our findings demonstrate that there are several gaps between the current practice and the standard. At the present time, operator involvement is not sufficient according to the regulatory requirements of internal control. Although the approach improves critical steps in the risk assessment procedure, it remains to be implemented in the fish farming industry.
Working at sea presents a considerable fatal accident risk to the fishers of the fleet, far exceeding any average land-based job, as far as incident rate per man-labor year. This no-doubt is the result of placing an industrial workplace on a moving, oscillating and inherently unstable working platform subject to the vagaries of the sea. Here hazardous work is conducted in cramped workspaces, on uneven, slippery and cluttered decks, with operations involving heavy fishing gears and mobile rotational machinery. The purpose of this article is to analyze the circumstances of the 281 deaths reported to the Norwegian authorities from the Norwegian fishing fleet over the period 1990–2011. The aim is to determine important characteristics and traits in the statistics, and to facilitate and focus the devising of future preventative intervention strategies. The results indicate that there has been a considerable reduction in the number of fatalities in the fishing fleet of Norway over this time frame. The average incidence rate for fatalities during the 22year study period is 8.71 per 10,000 man years. Single, fatality occurrences are a more important cause of deaths in the fleet than multiple casualty events. The most significant modes of fatality are vessel disasters followed by man overboard events and drowning in port. The findings lead to a discussion on both the significance of the preventative measures put in place over the past two decades and the future directions and evolutions required for continued success in reducing fatalities in the Norwegian fleet.
The fish farming industry is one of the industries in Norway with the highest occupational fatality and injury rate. Despite the serious health, safety, and environmental issues in the industry, little is done to measure changes in safety over time beyond the traditional Lost Time Injury (LTI) registrations. In this article the objective is twofold; (i) to propose a framework for developing safety indicators based on Systems-Theoretic Process Analysis (STPA), and (ii) to apply the framework to find indicators relevant for hazards in operations where subcontractors participate. STPA uses a hierarchical portrayal of the system in focus, in contrast to sequential models, and views safety as a control problem. It is believed that a systemic approach to indicator development better captures the complex safety challenges in aquaculture. Thirteen indicators are identified within areas such as maintenance, training, and planning. The indicators identified may function as a basis for decisions and actions that must be undertaken to ensure safe operations.
This paper presents an on-going research project focusing on the development of technology to enable autonomy in ROV operations. The project is a collaborative project between Norwegian offshore industry and academia. Currently, there is a large focus in research on the development of navigation, guidance and control for autonomous underwater vehicles (AUV). This is important as there will be a future demand for subsea inspection, maintenance and repair (IMR) operations with non-cabled systems. A future scenario is to have AUVs stationed on the seafloor in subsea garages. However, state of the art for IMR operations on the Norwegian Continental Shelf is to apply vessel supported ROVs in IMR operations. Efficiency in such operations will imply large cost and time savings. Increased autonomy enables the ROV operator to shift from manual to automatic control utilizing autonomous functions for a number of specific tasks. The research project presented in this paper is novel and the goal is to improve the capabilities of the ROV leaving the operator mainly to supervise operation. The paper discusses different aspects of the technology requirements. This may be useful for researchers working in the area of AUV research, relating this research to industrial needs. The presented project will develop novel integrated sensor platforms with robust perception methods and collision-free motion planning algorithms for subsea inspection and light intervention operations. Moreover, the project will also focus on subsea factory design enabling autonomous operations. The results will be tested, verified and demonstrated in full-scale test beds, as well as at an offshore location.
This article presents a virtual age model for decision support regarding life extension of ageing repairable systems. The aim of the model is to evaluate different life extension decision alternatives and their impact on the future performance of the system. The model can be applied to systems operated continuously (e.g., process systems) and systems operated on demand (e.g., safety systems). Deterioration and efficiency of imperfect maintenance is assessed when there is limited or no degradation data, and only failure and maintenance data is available. Systems that are in operation can be studied, meaning that the systems may be degraded. The current degradation is represented by a “current virtual age”, which is calculated from recorded maintenance data. The model parameters are estimated with the maximum likelihood method. A case study illustrates the application of the model for life extension of two fire water pumps in an oil and gas facility. The performance of the pump system is assessed with respect to number of failures, safety unavailability and costs during the life extension period.
No abstract is provided for this article.
Autonomous and unmanned ships are approaching reality. One of several unsolved challenges related to these systems is how to perform safety verification. Although this challenge represents a many-faceted problem, which must be addressed at several levels, it seems likely that simulatorbased testing of high-level computer control systems will be an important technique. In the field of reliability verification and testing, design verification refers to the process of verifying that specified functions are satisfied over the life of a system. A basic requirement for any autonomous ship is that it has to be safe. In this paper, we propose to use the Systems-Theoretic Process Analysis (STPA) to (i) derive potential loss scenarios for autonomous ships and safety requirements to prevent them from occurring, and (ii) to develop a safety verification program, including test cases, intended to verify safety. Loss scenarios and associated safety requirements are derived using STPA. To derive a safety verification program, these unsafe scenarios and safety requirements are used to identify key variables, verification objectives, acceptance criteria and a set of suitable verification activities related to each scenario. The paper describes the proposed methodology and demonstrates it in a case study. Test cases for simulator-based testing and practical sea-trials are derived for autonomous ships. The case study shows that the proposed method is feasible as a way of generating a holistic safety verification program for autonomous ships.
