This article introduces Quality Function Deployment (QFD) as a method for improving the environmental performance of the Norwegian fishing fleet. Systems engineering has been introduced as a feasible process for handling sustainability issues in the fisheries, because it contains methods for general system design, operation, and support in a life-cycle perspective. QFD is related to systems engineering as a method for translating stakeholder needs into detailed system requirements at each life-cycle stage. Eco-QFD extends the scope of QFD, and combines QFD, Life-Cycle Cost (LCC), and Life-Cycle Analysis (LCA) to evaluate environmental effects and costs in the system development process. The article assesses the usefulness of Eco-QFD in fisheries management decision-making regarding sustainability in the fishing fleet, and for shipyards in their design of fishing vessels. It is concluded that Eco-QFD may be difficult to use for fisheries management in its present form, due to the complexity of sustainability, and the time and efforts demanded to carry out the analyses. Nevertheless, the structuring of the stakeholder needs and requirements may contribute to improved understanding of the decision-situation.
Many fisheries have significant challenges related to sustainable development, such as overexploitation and overcapacity in the fishing fleet. Overcapacity leads to increased pressure on fish resources, reduced profitability, and environmental problems such as greenhouse gas (GHG) emissions and acidification fromfuel consumption. Sustainable management of the fish resources is an important objective in Norway, but overcapacity is a problem in several Norwegian fleet segments. Important issues in this respect are whether the traditional management models are able to deal with the capacity development, and whether the role of technology as a relevant discipline in fisheries management is underestimated.The objective of this work has been to integrate a technological perspective into fisheries management in order to improve sustainability in the fishing fleet. The thesis work has been limited to the Norwegian fisheries in Norwegian territorialwaters. Since the main problems addressed in this thesis are sustainability and overcapacity, the system boundaries are limited to the fishing fleet. This means that the marine ecosystem in where the fishing vessels are interacting, is outside the thesis’ boundaries.The main contributions of this thesis are:• Development of a methodological framework that structures fisheries management decision-making, with main emphasis on improved sustainability in the fishing fleet.• Clarification of the concept of sustainability in the Norwegian fishing fleet.• Classification of attributes characterizing sustainability, and a performance evaluation of the different vessel groups in the cod-fishing fleet.• Comparison of two cod-production systems, with focus on sustainability.• Suggestions for how fisheries management can evaluate sustainability on a regular basis.• Improved foundation for further research about sustainability in the fisheries. A lot of literature is collected and synthesized.The framework developed is based on the systems engineering process. The nature of sustainability requires a systems perspective. There are different system analysis methods, but from a technological perspective, dealing with multidisciplinary tasks, systems engineering has been selected as the most feasible process. It has a strong focus on stakeholder needs and requirements, and it facilitates frequent evaluations of sustainability, which is important in order to assess management efficiency and goal achievement.Problems regarding sustainability in the fisheries are not only caused by technological development, but have organizational challenges as well. However, in this thesis the focus is within the technological perspective. Systems engineering is not applied as an attempt to change the structure of fisheries management, but as means of suggesting a decision-making process that improves sustainability in the fishing fleet.Fisheries management involves decision-making in situations often characterized by high risks and uncertainties, and it may be difficult to predict the outcomes of the decisions, for example, regarding sustainability in the fishing fleet. A number of tools that are available to support decision-making have been discussed and used in the thesis, such as cost-benefit analysis, risk acceptance criteria, life cycle cost (LCC), the Analytic Hierarchy Process (AHP), and Quality Function Deployment (QFD). Nevertheless, these tools do not provide “correct” answers; they have limitations, they are based on a number of assumptions, and their uses are based on scientific knowledge as well as value judgments involving political, strategic, and ethical issues. This means that these methods leave the decision-makers to apply decision processes outside the practical applications of the analyses, to which the framework offers guiding principles and structure.The main outcome of using systems engineering principles in fisheries management, is that the framework offers a broader analytical perspective to fisheries management and sustainability, which acknowledge that sustainability cannot be distinguished fromthe context. Today, most input to fisheries management come from biology and economy, such as stock assessments and profitability analyses. In systems engineering, information from different scientific disciplines, for example, biology, social sciences, economy, and technology, are necessary input to the analyses and decision processes, because fisheriesmanagement is much more than bio-economics. Application of the systems engineering process in fisheries management, and the inclusion of technology, introduce new perspectives, new disciplines, and new stakeholders into the decision-making process in the fisheries.Based on the framework developed in the thesis, the sustainability performance of the cod-fishing fleet has been evaluated. Sustainability in the fishing fleet may be characterized by seven attributes; accident risk, employment, profitability, quality, catch capacity, bycatch/selection, andGHGemissions/acidification. Indicators have been identified in order to measure the system performance within the attributes. The evaluation shows that there are differences in the performance of the vessel groups. These differences pose a major challenge to fisheries management in their decision-making regarding sustainability in the fleet. The smallest vessels have the lowest fuel consumption (kg fuel/kg fish), but they have a very high accident risk (FAR). The evaluation of cod fishing vs. cod farming shows that the potential growth in the cod farming industry may cause changes in the management system of the cod fisheries, such as a possible shift from the IVQ-systemof today to an ITQ-system.The Norwegian fisheries management lacks frequent evaluations of its policies, and the information and data available about the fisheries are fragmented. Sustainability should be evaluated on a regular basis by use of performance indicators to determine if sustainability increases or decreases. For simplicity, the indicators could be aggregated into a sustainability index showing the overall system performance. Aggregation implies simplification and weighting of the indicators, which means that such an index should be used with care. Sustainability implies a long term perspective when taking decisions, because future generations will be affected. The performance evaluations can give indications of trends, which means that the results can be used to predict consequences in the future, based on the current development.
No abstract is provided for this article.
Decarbonization is a trend in the maritime industry and may include the use of alternative energy sources on ships. At the same time, autonomous ships are under development. In the future, the two technologies may be combined. The objective of this study is to identify possible hazards related to the operation of autonomous vessels using green energy sources. An extended and holistic Systems-Theoretic Process Analysis (STPA) based approach is proposed, where both safety and security is considered. Changes in level of autonomy during operation are considered, and an extension of the STPA method is proposed to highlight the interaction between the system and external energy source. A solar-powered and wave-propelled unmanned surface vehicle is analysed. The results show that mission performance may be affected by both safety and security issues, and that considering influences from the environment and the autonomous functionalities of the system together, contributes to identifying hazards. The results are compared to operational experience from multiple field campaigns. The case study focuses on a relatively simple autonomous vehicle, but some functionalities may be shared with Maritime Autonomous Surface Ships (MASS). Hence, implications for utilisation of alternative energy sources on MASS, and effects on risks, are discussed.
As autonomous ships become more viable, appropriate risk indicators are increasingly needed. In the existing literature, few works are related to developing such risk indicators. Existing indicators are general and not focused on a specific autonomous vessel, let alone an actual operating ship. To bridge this research gap, this article proposes a methodology for identifying risk indicators based on a Bayesian belief network (BBN). The methodology is applied to hazardous event "losing navigational control" of a trial-operating autonomous passenger ferry. The risk indicators developed cover technical equipment, remote supervisors' capacity, and environmental conditions. The probability of losing navigational control is calculated considering the states of the risk indicators, which contributes to risk monitoring of the system during operation. Further, strong wind, the state of battery health, end-to-end delay, and packet-loss rate have been identified as critical risk indicators. These should preferably be presented in a shore control center to human operators and provide a basis for decision support, and for defining operational procedures for safe takeover and shared autonomy. The findings can further improve practitioners' understanding, monitoring, analysis, and management of the risk of loss of navigational control of autonomous ships, which is crucial to prevent collisions.
The objective of this article is to present a method for developing collision risk indicators applicable for autonomous remotely operated vehicles (AROVs), which are essential for promoting situation awareness in decisions support systems. Three suitable risk based collision indicators are suggested for AROVs namely, time to collision, mean time to collision and mean impact energy. The proposed indicators are classified into different thresholds; low, intermediate and high. An AROV flight path is simulated to gather input data to calculate the proposed indicators and three collision targets are established, i.e., subsea structure, seabed and a cooperating AROV. The proposed indicator development method together with the case study show a proof-of-concept that the combination of mean time to collision and mean impact energy indicators can identify risk prone waypoints in the AROV path. The method results in an overall risk picture for a given AROV path. The results may provide useful input in replanning of mission paths and for implementation of risk reducing measures. Even though the method focuses on collision risk, it can be used for other accident scenarios for AROVs.
Compared to conventional marine systems, where the onboard crew can perform frequent and flexible maintenance, autonomous marine systems (AMS) only involve a limited (or, even no) crew during a voyage, and this challenges maintenance planning and execution. The current study identifies the relevant issues and proposes to solve these through developing a dynamic maintenance planning method for AMS. By considering economical dependencies among components, the study presents a dynamic grouping method to determine the optimum maintenance opportunities for AMS in the future. Stochastic dependencies of components are considered by using the Markov model. A multiphase Markov model is proposed for modeling stochastic dependencies between components where the limited and irregular maintenance opportunities are handled by the multiphase part of the model. A heuristic method is proposed to deal with the combinatorial challenge. To demonstrate the application of the proposed method, the maintenance planning of a cooling system of an autonomous ship is performed in a case study. To validate its performance, the proposed heuristic method is compared with existing 'short-sighted' methods for a selection of candidate groups for maintenance. In the validation, various scenarios with different component states and maintenance strategies are tested.
During the last decade, increasing attention has been focused on environmental protection. For instance, the ecological effects of hydrocarbon releases in the sea are of paramount concern. One way to assess their environmental impact is to consider the amount of pollutant discharged. Effective early detection would help in revealing spills in advance and take the necessary mitigating measures to contain the released volume. Standards and guidelines are established for developing effective sensor networks in the subsea templates for monitoring purposes and data collection. Sensors provide a heterogeneous amount of information about the template they are monitoring. According to recent studies on risk assessment, the level of knowledge about a specific system is an intrinsic feature that should be considered during the assessment and evaluation phases for better managing potential increments of the risk level. The information provided by sensor networks may be used in this perspective. Sensors may be functionally placed in fault tree analyses and update the information about frequency deviation. The work in this paper is focused on risk management using such information from subsea sensor networks. A real reference case from the oil and gas industry located in an environmentally sensitive area on the Norwegian Continental Shelf is provided for testing the suggested approach. The case study refers to subsea monitoring of oil leakages from the wellhead templates. Insights from the case study highlight how sensor data analysis may improve risk management and support operational decision making.
The introduction of autonomy in subsea operations may affect operational risk related to Inspection, Maintenance, and Repair (IMR). This article proposes a Bayesian Belief Network (BBN) to model the risk affecting autonomous subsea IMR operations. The proposed BBN risk model can be used to calculate the probability of aborting an autonomous subsea IMR operation. The nodes of the BBN are structured using three main categories, namely technical, organizational, and operational. The BBN is tested for five unique scenarios using a scenario generation methodology for the operational phase of the autonomous IMR operation. The BBN is quantified by conducting a workshop involving industry experts. The results from the proposed model may provide a useful aid to human supervisors in their decision-making processes. The model is verified for five scenarios, but it is capable of incorporating and calculating risk for other combinations of scenarios.
Advanced technological systems consist of a combination of hardware and software, and they are often operated or supervised by a human operator. Failures in software-intensive systems may be difficult to identify, analyze, and mitigate, owing to system complexity, system interactions, and cascading effects. Risk analysis of such systems is necessary to ensure safe operation. The traditional approach to risk analysis focuses on hardware failures and, to some extent, on human and organizational factors. Software failures are often overlooked, or it is assumed that the system's software does not fail. Research and industry efforts are directed toward software reliability and safety. However, the effect of software failures on the level of risk of advanced technological systems has so far received little attention. Most analytical methods focus on selected software failures and tend to be inconsistent with respect to the level of analysis. There is a need for risk analysis methods that are able to sufficiently take hardware, software, and human and organizational risk factors into account. Hence, this article presents a foundation that enables software failure to be included in the general framework of risk analysis. This article is the first of two articles addressing the challenges of analyzing software failures and including their potential risk contribution to a system or operation. Hence, the focus is on risks resulting from software failures, and not on software reliability, because risk and reliability are two different aspects of a system. Using a functional perspective on software, this article distinguishes between failure mode, failure cause, and failure effects. Accordingly, 29 failure modes are identified to form a taxonomy and are demonstrated in a case study. The taxonomy assists in identifying software failure modes, which provide input to the risk analysis of software-intensive systems, presented in a subsequent article (Part 2 of 1) (Thieme et al.).
Purpose – Maintenance planning is a complicated decision-making process that involves the major stakeholders and the main life-cycle phases of an engineering system. The purpose of this paper is to propose an availability-centred maintenance planning approach for offshore wind farms, with special focus on the early system design phase. Design/methodology/approach – The proposed approach is based on a stepwise procedure that integrates logistics consideration into reliability-centred maintenance. For each step, the essential methods for systematic analysis and documentation are introduced. Findings – Practical information from current offshore wind farms and lessons learned from relevant industries are included to exemplify and justify the implementation of the proposed approach. In a general way, the approach shows that valuable input can be provided to decision making about maintainability and maintenance planning. Furthermore, the approach facilitates the initial maintenance plan to be adjusted and improved upon as additional operating experience becomes available. Research limitations/implications – Offshore wind energy is still an industry in its infancy with an attendant high degree of confidentiality. There is scarcely any detailed practical information available for the production of a case study on this topic. However, the current paper’s theoretical basis may be applied to identify current and future knowledge gaps, for the development of more detailed guidelines as established in the further research. Originality/value – Maintenance planning of offshore wind farms is an area of current interest, although often the focus is on achieving cost reductions and not on the formal development of such a systematic approach as conceived in this paper.
Environmental concerns, emission regulations, fuel prices, and emission taxes increase the demand to improve energy efficiency in shipping. However, several barriers prevent the adoption of cost-effective energy saving measures. In this article a framework is offered to overcome the barriers encountered in shipping. 12 participants from five ship owners in Norway, two equipment suppliers, and a research institute have provided input to this study. The framework makes the barriers evident to ship owners and (energy) managers. It helps them to prioritize and overcome the critical barriers to improve energy efficiency in a consistent manner. Researchers and policy makers can also utilize the framework as it makes challenges to energy efficiency apparent. Finally, due to its generic structure it can be applied to industries other than shipping.
No abstract is provided for this article.
No abstract is provided for this article.
Marine system accidents have a relatively high frequency worldwide. In Norwegian waters loss of installations has been avoided the last 20 years, but several near-misses and severe incidents confirm that the risk level is not insignificant. Barriers should prevent undesired events or reduce consequences should such events occur. The main purpose of barrier management is to establish and maintain the necessary barriers. It includes the processes, systems, solutions and measures needed to ensure implementation and follow-up of barriers. Petroleum Safety Authority has emphasized the need to develop barrier strategies during the last few years, and this has been particularly emphasized for topside systems and barrier functions to prevent and/or mitigate the consequences of hydrocarbon (HC) leaks. Insofar, the same focus on barrier management has not been put on marine systems and structural hazards. Risk associated with ballast systems, anchoring systems and dynamic positioning is at a level where further improvements should be made. Independent barrier elements and/or functions are required in order to provide substantial risk reduction. Some suggestions for independent barriers are discussed in this paper.
This paper employs a combination of literature review and case study methodology to assess the gap between current remotely operated vehicle (ROV) standards and future autonomous IMR operation requirements. With advent of autonomous subsea and underwater vehicle systems, current ROV standards and guidelines may not offer the same benefit in designing and setting guidelines for safe autonomous operations. The reasons for this claim are two-fold. Firstly, the literature review shows that existing requirements in the ROV standards lack specifications related to autonomous subsea interventions. Secondly, the results from the case study demonstrates existence of knowledge and technology gaps, which pose challenges in development of future autonomous IMR operations.
No abstract is provided for this article.
The article introduces a general method for developing a Bayesian Network (BN) for modeling the risk of maritime ship accidents. A BN of human fatigue in the bridge management team and the risk of ship grounding is proposed. The qualitative part of the BN has been structured based on modifying the Human Factor Analysis and Classification System (HFACS). The quantitative part is based upon correlation analysis of fatigue-related factors identified from 93 accident investigation reports. The BN model shows that fatigue has a significant effect on the probability of grounding. A fatigued operator raises the probability of grounding of a large ship in long transit with 23%. Compared to the two watch system (6–6 and 12–12), the 8–4–4–8 watch system seems to generate the least fatigue. However, when manning level, which is influenced by the various watch schemes, is taken into account, the two watch system is preferable, leading to less fatigue and fewer groundings. The strongest fatigue-related factors related to top management are vessel certifications, manning resources, and quality control.
In this chapter, the online risk levelRisk level of different dynamic positioningDynamic positioning (DP) systems is assessed based on the methods presented in Chap. 2 . The proposed methods are able to provide an online risk levelRisk level of the system in different operating and environmental conditionsEnvironmental conditions that could assist decision makers to make better (safer) decisions. In the first example, (All proprietary, identification, sensitive, and confidential information are removed.) Subsect. 7.1, a DP drilling unitDrilling unit incidentIncidents is simulated. In this incidentIncidents, the operator has two choices of manual or automaticAutomatic disconnectionDisconnection. A risk modelRisk model is utilized to estimate the risk levelRisk level of these two scenarios and provide useful information to the operator to select the safer option. In this example, two operators with different behaviors are compared, and the effect of human performance on the system risk levelRisk level and scenario selection is discussed. In the second, third and fourth examples (AutomaticAutomatic mode: the DP system automatically maintains the vessel position using the control system and related actuators.) (Subsect. 7.2, 7.3, and 7.4, respectively), supervised failure scenario generationSupervised failure scenario generation is presented. In these examples, different DP incidentsIncidents are investigated, initial eventsInitial event, operating and environmental conditionsEnvironmental conditions are used as risk modelRisk model inputs, and the modelModel predicts the most probable failureFailure scenarios. In each example, the most probable failureFailure scenarios are compared with real incidentsIncidents. The generated scenarios could help operators to make better decisions to avoid most probable failureFailure scenarios.
