In this chapter, the scope, objective, and limitations of this book are discussed. First, the problem under study and background of the risk assessmentRisk assessment in automated and autonomousAutonomous marine systems are presented. Then, consideration and limitations of safetySafety and risk assessmentRisk assessment are discussed, and the dynamic positioningDynamic positioning (DP) system as an example of an advanced and complex systemComplex systems relevant for autonomousAutonomous marine and maritime systemsMarine and maritime systems is presented. The chapter is finalized by presenting the book structure.
The analytic hierarchy process has been used to elicit the knowledge of maritime transport stakeholders, such as seafarers, authorities, insurers and academics, regarding human factors and risk-reducing measures for ship groundings. Measures against human fatigue, alcohol abuse, language barriers, poor bridge management and safety climate have been compared with regard to costs and benefits. The measures are discussed in the context of large ships on a voyage of at least 24 h containing Bridge Resource Management. The study shows that stakeholders consider the costs of the measures to reduce human fatigue at sea surpass the benefits. Measures against alcohol abuse are regarded as the most cost–benefit efficient. Also, the stakeholders consider the watch scheme 8–4–4–8 to be less fatiguing than the 12–12 watch scheme. In addition, the results of the study support previous findings that inadequate manning levels contribute the most to human fatigue within the bridge management team.
No abstract is provided for this article.
A Dynamic Positioning (DP) system enables vessels and rigs to accurately maintain a predetermined position and heading or track. It enables precise operations under harsh environmental conditions. DP is used for a variety of purposes; however, the role of the DP operator (DPO) is considered the same regardless of type of operation: to monitor and keep the vessel in position. Some of the decisions that the DPO makes are safety critical, for example, decisions about the set-up of the system can prevent the vessel from colliding with an offshore oil and gas platform. Applied cognitive task analysis (ACTA) is performed to analyze how the different operational settings influence the role and decision-making of the DPO. Two DPOs with experience from five different operation types were interviewed. The results from the ACTA for the different operation types are compared with respect to technical steps, cues, the cognitive steps and components, actions, and decisions. The contextual factors are evaluated using an adapted version of Rasmussen's dynamic safety model. The results of the comparison are used to evaluate the current role of the DPO, in light of the DP system and different DP operations. Recommendations for the improvement of safety, the design of the DP system, training and set-up of DP operations are formulated.
This paper's objective is to analyze the main real-time risks in operation of autonomous marine systems, which follow from various levels of autonomy (LoA). High reliability management (HRM) is an established framework for assessing real-time operator performance in complex infrastructures. In this paper, the framework is applied to two cases representing different uses and autonomy levels: one on marine underwater robotics focusing on remotely operated vehicles (ROVs) and subsea intervention, and the other addressing operation of a complex marine surface vessel with a dynamic positioning (DP) system. Usually, autonomous systems are associated with unmanned systems, but several manned systems (for example, ships with complex automation and DP systems) have specific control functionality that can be characterized as autonomous. This paper focuses on manned and unmanned systems with different levels of autonomy and major hazard potential. The most important research finding is having identified multiple, different operational states that vary across two or three LoAs, each state of operations having significantly different risks to be managed in real time. The application of the HRM framework highlights the importance of enabling reliable operator control and online risk management in the development of next generation autonomous marine systems.
Shore control center (SCC) operators of maritime autonomous surface ships (MASS) may be allocated with complex responsibilities within different degrees of autonomy (DoA), from remote control to remote supervision, and the role of human in-the-loop is significant. In the current research on MASS, however, the safety of interactions between humans and systems are not sufficiently considered. Hence, this paper proposes a system-theoretic approach to safety analysis for human-system collaboration. The novelty of the approach is the definition of operational contexts of MASS and the integration of a human cognitive model into the system theoretic process analysis (STPA), called STPA-Cog. The method is demonstrated in two case studies of MASS: (i) remote control mode (RCM) and (ii) remote supervision mode (RSM), focusing on two types of navigational accidents (i.e., collision and grounding). The analysis results are derived by comparing the human-related and technical-related causal scenarios in RCM and in RSM. The findings can assist in human-oriented design and operational planning for SCC of MASS. Further, the proposed approach also has the potential to be used and extended to systemic safety analysis in other intelligent transportation systems.
Climatic degradation of equipment, in combination with stringent requirements for human safety and minimalistic environmental impact, need to be addressed through improved risk assessment in vulnerable areas, such as the Arctic. The performance of technologies and risk related to its utilization, for example in terms of autonomous operations, significantly impact future requirements for oil and gas exploration and production. An interdisciplinary and systemic approach integrating both risk to the environment and to humans is needed as the challenges related to operation in extreme environments directly impact risk, costs, and the general societal acceptance of the activities. Development of such an approach focusing on autonomous underwater vehicles (AUV) and operations is addressed in this paper.
Purpose The purpose of this paper is to present an approach for measuring the ability of oil and gas production plants to utilize shutdowns opportunistically for maintenance. Design/methodology/approach Key performance indicators have been developed from case studies with two offshore oil and gas installations on the Norwegian Continental Shelf. The key performance indicators measure the quality of the work preparations and the ability to utilize shutdowns opportunistically. Shutdowns may provide opportunities for execution of maintenance, but it is hardly possible to undertake any maintenance work requiring shutdown if the organization is not well prepared and the work is not well planned. Findings The results from testing of the indicators on two oil and gas installations shows that several of the indicators are relevant for determining the quality of preparations, whereas more effort needs to be put into gathering data applicable for monitoring the actual utilization of the shutdowns. Research limitations/implications Production losses, due to turnarounds and unforeseen shutdowns in oil and gas operations, are significant, and the improvement potential is large. The indicators may assist maintenance managers in planning and improving the plant's utilization of shutdowns and may contribute to substantial cost savings. Originality/value The approach in the paper adds important knowledge on how to actually measure the quality of maintenance work planning and execution.
Extending the operating lifetime of ageing technical systems is of great interest for industrial applications. Life extension requires identifying and selecting decision alternatives which allow for a safe and economic operation of the system beyond its design lifetime. This article proposes a dynamic Bayesian network for assessing the life extension of ageing repairable systems. The main objective of the model is to provide decision support based on the system performance during a finite time horizon, which is defined by the life extension period. The model has three main applications: (i) assessing and selecting optimal decision alternatives for the life extension at present time, based on historical data; (ii) identifying and minimizing the factors that have a negative impact on the system performance; and (iii) reassessing and optimizing the decision alternatives during operation throughout the life extension period, based on updating the model with new operational data gathered. A case study illustrates the application of the model for life extension of a real firewater pump system in an oil and gas facility. The case study analyzes three decision alternatives, where preventive maintenance and functional test policies are optimized, and the uncertainty involved in each alternative is computed.
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.
In this attachment, the specific faultFault trees for different types of dynamic positioningDynamic positioning (DP) systems are presented and compared.
In this paper, we propose and demonstrate a method for risk-based path planning, generating risk-acceptable trajectories for an autonomous surface ship. This objective is achieved in two steps. The first step uses a dynamic probabilistic risk assessment method called K-shortest-paths Probabilistic Risk Assessment (KPRA), alongside risk models and dynamic system simulation, whose output is a list of paths ranked with respect to risk. Multiple risk influencing factors and accident types may be considered in this step. In this work, collision and powered grounding accidents, and their risk-influencing factors and indicators, are considered. The second step is to perform risk evaluation on the ranked scenarios, comparing them to predefined risk acceptance criteria and defining which scenarios have acceptable risk. Evaluating multiple paths allows operators to choose how strict the autonomous ship's decision-making should be regarding risk, avoiding the problem of minimization algorithms being too conservative in terms of risk. One of the risk-acceptable paths is then chosen as a local plan to be acted by the autonomous ship. This paper has two main scientific contributions. First, an online Bayesian belief network risk model for navigation accident probability is designed and integrated to improve KPRA. Second, we propose a modified guidance, navigation, and control framework for autonomous ships, to include risk-based planning. We implement the modified framework into a real autonomous surface vehicle (ASV) named Grethe and demonstrate the method's capabilities in field trials, where the ASV uses the method to generate local plans that avoid possible collision and grounding accidents.
The article introduces a general method for developing a Bayesian Network (BN) for modelling human fatigue at sea. A BN of human fatigue in the bridge management team 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 probability of human fatigue amongst the ship's bridge personnel is calculated to be 0,23. The strongest fatigue—related factors related to top management are vessel certifications, manning resources, and quality control.
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.
