This study uses comparative life cycle assessment to compare the life cycle impacts of three power systems for a 10.5-m Norwegian gillnet fishing vessel: a conventional diesel-mechanical (DMS), a diesel-battery parallel hybrid (PHS), and a hydrogen fuel cell-battery series hybrid (SHS) system. Results show that direct emissions are dominated by combustion-related impacts, while component manufacturing drives toxicity and resource categories. The PHS reduces global warming potential (GWP) by 28%, and the SHS by up to 91% compared to the DMS when powered by low-carbon electricity and green hydrogen from electrolysis in Norway. However, both alternatives shift burdens toward manufacturing-related impact categories. Sensitivity analysis highlights the critical role of electricity and hydrogen supply pathways: fossil-based inputs can erase benefits, while localised low-carbon electrolysis enables slight improvements. The findings stress that battery and fuel cell systems can reduce fishing vessel emissions meaningfully only when coupled with clean energy supply chains. • Comparative LCA of power system alternatives for a coastal fishing vessel. • Hybrid battery–diesel cuts life-cycle GWP by about 28%. • Fuel cell–battery hybrid lowers GWP by up to 91% with clean energy. • Results hinge on electricity mix and hydrogen supply pathways. • Manufacturing adds toxicity and resource use trade-offs.
A working fishing vessel at sea is an elaborate collection of interacting accident potentials, barely controlled. Even the deck underfoot betrays the unwary, as can every other aspect of the normal daily grind onboard, as fishers ply their trade in weather foul and fair. All elements of the vessel at sea conspire in making this the most dangerous and difficult of all professional callings, an inexplicable calling where life and limb are continually at risk. This article is based on an examination of reported occupational injuries from the Norwegian fishing fleet from 2000 to 2011. The aim is the determination of important characteristics and traits in the statistics, which may be used to focus and further preventative measures to be applied within this fleet. The results indicate that the current intervention programs and improvement measures have to date made a significant impact on injury levels within the fleet. This study has borne witness to a reduction in injury numbers and incident rates year on year for the past 12 years. It identifies the trawler fleet as the seat of the highest incident rates of injury occurrence, while the small coastal fleet had the lowest reported numbers of injuries. Under-reporting of minor injuries is revealed as a problem in the current reporting system of fisher injuries while the manner, location and body regions of reported injuries are also investigated. These findings lead to a discussion on the future requirements for the Norwegian fleet for further injury reduction and improved reporting practices.
This article discusses the performance of the Norwegian fishing fleet within an acceptable level of sustainability. Previously, the cod-fishing fleet has been evaluated at the attributes—accident risk, employment, profitability, quality of the fish meat, catch capacity, greenhouse gas emissions/acidification, and bycatch/selection. The assessments focused on the first four steps of the systems engineering process, i.e., from needs identification to trade-offs of system alternatives. The objective of this paper is to focus on the last steps of the process; design, solve, verify, and test, to improve the decision-basis for fisheries management in order to increase sustainability in the fishing fleet. More specifically, this means to analyze the decision-making situation and develop acceptance criteria of a sustainable Norwegian cod-fishing fleet to enable fisheries management to monitor the sustainability performance of the fleet on a regular basis.
This article presents the process used to develop safety envelopes and subsea traffic rules for autonomous remotely operated vehicles (AROVs) used in subsea inspection, maintenance, and repair (IMR) operations. Preventing loss of subsea assets and the AROV is the overall goal of the proposed safety envelopes and subsea traffic rules. Currently, no such envelopes and rules exist. The safety envelope for the AROV is constructed using an Octree method. The proposed subsea traffic rules are derived by combining existing traffic regulations in marine and aviation industries. The proposed safety envelopes and traffic rules are tested using both a novel modular open robot simulation engine (MORSE) based underwater simulator and in the laboratory. The results from the laboratory tests show that the proposed safety envelopes and subsea traffic rules can be used during simulated or real IMR operations to recommend subsea traffic rules to the AROV and the human supervisor.
No abstract is provided for this article.
The International Workshop for Autonomous System Safety (IWASS) is a joint effort by the B. John Garrick Institute for the Risk Sciences at the University of California Los Angeles (UCLA-GIRS) and the Norwegian University of Science and Technology (NTNU). IWASS is an invitation-only event designed to be a platform for cross-industrial and interdisciplinary effort and knowledge exchange on autonomous systems' Safety, Reliability, and Security (SRS). The workshop gathers experts from academia, regulatory agencies, and industry to discuss challenges and potential solutions for SRS of autonomous systems from different perspectives. It complements existing events organized around specific types of autonomous systems (e.g., cars, ships, aviation) or particular safety or security-related aspects of such systems (e.g., cyber risk, software reliability, etc.). IWASS distinguishes itself from these events by addressing these topics together and proposing solutions for SRS challenges common to different types of autonomous systems. IWASS 2022 was held on August 28th in Dublin, Ireland, and gathered 30 participants from 20 organizations from around the globe. In addition, a panel session at the European Safety and Reliability Conference (ESREL 2023) discussed the workshop's main conclusions and additional points with a larger audience. This report summarizes IWASS 2022 discussions. It provides an overview of the main points raised by a community of experts on the current status of autonomous systems SRS. It also outlines research directions for safer, more reliable and secure future autonomous systems.
No abstract is provided for this article.
Abstract This chapter presents methods for analysing interdependencies in critical infrastructures. In general, there are three groups of methods for analysing interdependencies; (1) conceptual, (2) model and simulation and (3) empirical and knowledge-based approaches. Examples of methods belonging to these groups are presented. The latter part of the chapter discusses challenges related to modelling, focusing on how to deal with complexity, trade-offs between abstraction and fidelity, choice of consequence measures and obtaining information.
This article addresses the present status of seafood-oriented environmental methods and analyses, and pinpoints areas for further development. A recent study of the CO2 emissions associated with the production of farmed salmon in Norway, following the life cycle from hatching to consumption, is presented. The study was initiated due to the increased focus on environmental impacts from food production among consumer organizations, retailers, and authorities. In general, several methods are being currently applied to measure environmental performance. Unfortunately, different methods provide quite different results. An additional challenge is that most of these methods were originally developed for land-based production. If assumptions about the performance of the seafood industry are established on the basis of incorrect information, consequences for both the management and the market level may not be desirable.
No abstract is provided for this article.
The risk caused by DP vessels in offshore marine operations is not negligible, due to wide applications of DP vessels in complex marine operations, and the sharp increase of DP vessel population. The DP accidents/incidents on the Norwegian Continental Shelf (NCS) that have occurred after 2000 indicate a need for improving safety of DP operations, which calls for new risk reduction measures. The focus of this paper is particularly on the offshore loading operations with DP shuttle tanker in offloading from floating production storage and offloading (FPSO) vessels on the NCS, but the results may be relevant also for other types of DP vessels in offshore oil and gas operations. In the paper, Man, Technology and Organization (MTO) analysis is applied to investigate the cause and barrier failures of nine reported accidents/incidents occurring over a 16-year period (2000–2015). MTO is based on three methods, including structured analysis by use of an event- and cause-diagram, change analysis by describing how events have deviated from earlier events or common practice, and barrier analysis by identifying technological and administrative barriers which have failed or are missing. The results are categorized into technical failures, human failures, organizational failures, as well as a combination of failures. The main finding is that the majority of the accidents are caused by the combination of technical, human and organizational failures. Critical root causes, results of change analysis and barrier analysis, and combination of failures are focused in the discussion. Recommendations of potential safety improvements are made on the aspects of the assessment of the actual system function, barrier management for marine systems, risk information to support different decision-makings, and the development of an on-line risk monitoring and decision supporting system.
Numerous research and industry initiatives have increasingly aimed at developing maritime surface autonomous ships (MASS). Among the motivations for the use of MASS is the potential increase in safety when compared to traditional manned ships – particularly regarding human error. However, in spite of having less human intervention, MASS will rely on humans working on an onshore control center for their operation. There have been great advances in investigating the technical aspects of MASS operation, such as collision avoidance algorithms and detection sensors; nevertheless, possible human tasks and their deriving failures have rarely been addressed. This paper thus explores how humans can be a key factor for successful collision avoidance in future MASS operations. It presents a task analysis for collision avoidance through Hierarchical Task Analysis and making use of a cognitive model for categorizing the tasks. The failures in accomplishing these tasks are further analyzed, and human failure events are identified. The results provide valuable information for the design stage of the system; which must acknowledge the operators’ tasks to ensure a safe voyage. The conclusions of this paper are also a starting point for the implementation of a Human Reliability Analysis for this operation.
No abstract is provided for this article.
No abstract is provided for this article.
Enabling higher levels of autonomy while ensuring safety requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A simulation case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions.
<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>
