Maritime Autonomous Surface Ships (MASS) are the subject of a diversity of projects and some are in testing phase. MASS will probably include operators working in a shore control center (SCC), whose responsibilities may vary from supervision to remote control, according to Level of Autonomy (LoA) of the voyage. Moreover, MASS may operate with a dynamic LoA. The strong reliance on Human-Autonomous System collaboration and the dynamic LoA should be comprised on the analysis of MASS to ensure its safety; and are shortcomings of current methods. This paper presents the Human-System Interaction in Autonomy (H-SIA) method for MASS collision scenarios, and illustrates its application through a case study. H-SIA consists of an Event Sequence Diagram (ESD) and a concurrent task analysis (CoTA). The ESD models the scenario in a high level and consists of events related to all system's agents. The CoTA is a novel method to analyse complex systems. It comprises of Task Analysis of each agent, which are preformed concurrently, and uses specific rules for re-description. The H-SIA method analyses the system as whole, rather than focus on each component separately, allowing identification of dependent tasks between agents and visualization of propagation of failure between the agents’ tasks.
This chapter presents an approach for a cross-sector risk and vulnerability analysis (RVA) of critical infrastructures. The RVA is an extended version of a preliminary hazard analysis (PHA) and can be applied to any complex system with only minor adaptations. The analysis has three phases described below: (1) analysis preparation, (2) preliminary risk analysis and (3) detailed risk analyses. The objective of the RVA is to identify hazardous events related to the activity/system as thorough as reasonably practicable. In phase 2, risk is assessed by the analysis group from direct assessments of probabilities and consequences on a semi-quantitative scale, such as low (L), medium (M) and high (H). This is in line with a standard PHA, which aims to identify and assess all major risks, and provide risk-reducing measures, without including detailed risk calculations or analyses. The preliminary risk analysis is then used for screening, and the most critical events are investigated further for various detailed analyses and quantifications. The RVA described here intends to give a complete overview of all risks elements related to the systems under investigation.
More than 100 Floating Production Storage and Off-loading (FPSO) concepts are in operation at the moment worldwide, and due to increasing demand many are currently under construction or being converted from commercial tankers. Still, operational risk is an issue that is not considered thoroughly in the initial feed stage, especially when compared to fixed installations. Floating installations are more dependent on manual control of some of the marine systems during normal operations, as well as during critical situations. Accidents can be initiated by errors induced by human and organizational factors (HOF), technical (design) failures, environmental conditions, or a combination of all three. Therefore, effective means are extremely important to prevent or mitigate the effects of potential operational accidents that can result in serious accidents. Updated statistics in 2013 revealed that the frequency of operational accidents during the last ten years was significantly above the target value that has been aimed for, in fact it was concluded that the frequency was around one order of magnitude higher. Hence, the objective of this article is to assess the hazards, the existing barriers, the risk level, and the risk reduction potentials more thoroughly.
Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate risks associated with complex systems. However, current approaches to construct and execute DPRA models are challenged by high execution time owing to numerous possible scenarios. This issue will affect the execution time of the model, which is in contrast with the aim of modeling. DPRA models must be sufficiently fast to assist decision-making processes in the required time. In this study, a new method is proposed to enhance the execution times of DPRA models. This method uses optimization algorithms to determine failure scenarios and sort scenarios based on their occurrence probabilities. The most efficient optimization algorithms, considering the nature of the DPRA models, are mixed-integer sequential quadratic programming, modified branch-and-bound algorithm, and modified particle swarm optimization, which are then compared and discussed. To validate the effectiveness of this method, a simple case study is presented. The results show the effectiveness of the method, which has high accuracy and reduces the execution time significantly (e.g. execution time of risk assessment of 16,464 possible behavior scenarios after an incident in a dynamic positioning system is one fifth of the conventional methods). A detailed supervised DPRA model of dynamic positioning systems and its application on three incidents that occurred in the Norwegian offshore sector in previous years is presented in a subsequent article (Part 2 of 1) (Parhizkar et. al.). Case study results confirm that the supervised DPRA method can be applied to other complex systems so that the dynamic probabilistic risk values can be evaluated quickly and accurately.
An emergency is a situation that poses an immediate riskRisk to health, life, property, or environment. Most emergencies require urgent intervention to prevent a worsening of the situation, although in some situations, mitigation may not be possible, and agents may only be able to offer alleviating care for the aftermath. Online risk assessmentOnline risk assessment in emergencies should be performed in real-time, which means that dynamic probabilistic riskRisk should be assessed in a very short execution timeExecution time.
Monitoring the mechanical integrity (MI) of offshore oil and gas facilities is important. It enables early fault detection of emerging failure conditions and allows more time for planning and preparing remedial actions, which reduces revenue losses resulting from unnecessary and poorly coordinated maintenance actions. Static equipment, such as heat exchangers, constitutes crucial parts of offshore processing facilities. Heat exchangers are challenging to maintain, especially in ageing facilities, and major maintenance problems are the result of (a) poor designs, (b) poor utilization of process information, (c) poor interpretation of results, and (d) poor development and management of high‐quality maintenance and MI programs. This article addresses the most important technological, human and organizational challenges concerning monitoring of MI of shell and tube heat exchangers, and discusses recommendations for future improvements. A good MI program benefits the operation and maintenance of heat exchangers, and will lead to improved safety and reduced costs. © 2011 American Institute of Chemical Engineers Process Saf Prog, 2011
This article presents a new risk model for estimating the probability of allision risk (the impact between a ship under way and a stationary installation) from passing vessels on the Norwegian Continental Shelf (NCS). Offshore petroleum operators on the NCS are required by the Norwegian Petroleum Safety Authority (PSA) to perform risk assessments to estimate the probability of impacts between ships and offshore installations, both for field related and passing (merchant) vessels. This has typically been done using the aging industry standard COLLIDE risk model, but this article presents a new risk model based on a Bayesian Belief Network (BBN) that can replace the old COLLIDE model for passing vessels. The new risk model incorporates a wider range of risk influencing factors (RIFs) and enables a holistic and detailed analysis of risk factors, barrier elements and dependencies. Even though the risk of allision with passing vessels is very small, the potential consequences can be critical. The new risk model is more transparent and provides a better understanding of the mechanisms behind allision risk calculations. The results from the new model are aligned with industry expectations, indicating an overall satisfactory performance. The article discusses several key elements, such as the use of expert judgement to estimate RIFs when no empirical data is available, model sensitivity, and a comparative assessment of the new risk model to the old COLLIDE model.
Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, efficient human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.
Online risk assessmentOnline risk assessment methods confront multiple challengesChallenges when applied to complex automated and autonomousAutonomous systems. This chapter addresses the main challengesChallenges with online risk assessmentOnline risk assessment, related to methodology, data collectionData collection, uncertaintiesUncertainty, complexityComplexity and execution timeExecution time, and application for decision supportDecision support.
Considering that few or no human operators are directly involved in the operation of Autonomous Marine Systems (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision-making. The current study identifies the criteria for an online risk model for AMS, which can be used to assess its validity and effectiveness. Taking an under-ice Autonomous Underwater Vehicle (AUV) operation as an example, the current work investigates how different risk analysis methods, namely the Preliminary Hazard Analysis (PHA), the Systems Theoretic Process Analysis (STPA), and Procedural Hazard and Operability Analysis (HAZOP), contribute to fulfilling the different criteria for online risk modeling of AMS. The analysis results show that STPA can be considered a good basis for developing an online risk model due to its relatively good coverage of the identified evaluation criteria, especially its ability to handle the interaction between system and software failure. In addition, considering some shortcomings of using STPA and the changing role of human operators in the AMS operation, PHA and Procedural HAZOP can be used as complementary tools. It is expected that the analysis results and conclusions can be adapted to other AMS as well.
This article outlines a new approach to reliability, availability, maintainability, and safety (RAMS) engineering and management. The new approach covers all phases of the new product development process and is aimed at producers of complex products like safety instrumented systems (SIS). The article discusses main RAMS requirements to a SIS and presents these requirements in a holistic perspective. The approach is based on a new life cycle model for product development and integrates this model into the safety life cycle of IEC 61508. A high integrity pressure protection system (HIPPS) for an offshore oil and gas application is used to illustrate the approach.
Great efforts have been made in order to manage the fisheries more sustainably, but so far, most of these efforts have failed. This is putting the welfare of current and future generations at risk. The fishing fleets have catching capacity that well exceeds the rate at which ecosystems can produce fish, and thus many fish stocks are being overexploited. One of the objectives of the Norwegian government is to manage the fisheries in accordance with sustainable development. Sustainable development and risk management are frameworks with some mutual qualities. In the Norwegian petroleum industry, risk management of Health- Safety and Environment (HSE) is based on functional or goal-oriented regulations. Functional regulations focus on the result without describing in detail how it may be attained, e.g. an acceptable safety level at a petroleum installation. This paper discusses the possibility of transferring experience and knowledge of risk management and functional regulations from the Norwegian petroleum industry into the Norwegian fisheries management in order to increase sustainability in the fishing fleet. An important research question is the connection between an acceptable sustainability level in the fisheries, and transforming the fisheries regulations into functional regulations based on management objectives.
Understanding risk-influencing factors (RIFs) associated with the occurrence of maritime accidents is important to prevent their future occurrence, identify high-risk ships, and properly influence policy. However, current methods often suffer from selection bias or do not account for variations in ship exposure, leading to biased or incomplete assessments. This study addresses these gaps by incorporating AIS-derived activity metrics as offset variables in regression analysis. This transformation of the dependent variable leads to the analysis of accident rates. The method is applied to ship losses of command (i.e., loss of propulsion, loss of electrical power, or loss of directional control / steering) by cargo ships in Norwegian waters from 2017 to 2021. Significant variables influencing the rate of loss of command include ship’s flag state, ship manager domicile, number of inspection deficiencies, propulsion redundancy, and the use of a single fuel onboard. Inspection deficiencies and sailing with a flag of convenience are associated with increased rates. Sailing with a Norwegian ship manager, propulsion redundancy, and a single fuel type onboard are associated with decreased rates. Incorporating measures of ship activity as exposure significantly improves the overall model fit, leading to better identification of RIFs associated with the occurrence of ship losses of command. Sensitivity analyses using sailed distance as exposure and the Cox proportional hazards model demonstrate overall robustness. The results are beneficial for identifying high-risk ships from the perspective of vessel traffic management and can be used for decision-making. The method has promising potential for the future analysis of RIFs associated with other types of maritime accidents.
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.
On 16th April 2014, the MV SEWOL capsized in South Korea, and 304 persons died or went missing. This article describes the accident and finds causes from four different theoretical points of view: the energy-barrier model, Turner’s man-made disasters model, Rasmussen’s conflicting objectives perspective, and high reliability organizations theory. The results show that the theories together point out a total of 23 different causes to the accident. Different causes are identified from different theories and they complement each other. Finally, this article discusses a possible combination of the perspectives for improving both accident investigation and accident prevention.
