Publications

High reliability management and control operator risks in autonomous marine systems and operations

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.

Monitoring the mechanical integrity of heat exchangers

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

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

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.

A Systematic Approach to Risk Assessment: Focusing on Autonomous Underwater Vehicles and Operations in Arctic Areas

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.

An allision risk model for passing vessels and offshore oil and gas installations on the Norwegian Continental Shelf

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.

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

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.

Challenges of Online Dynamic Probabilistic Risk Assessment and Possible Solutions

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.

Optimization-based planning and control of AUVs applied to adaptive sampling under ice

This paper presents a framework for optimization-based informative planning and control with applications to adaptive sampling with AUVs under sea ice. A spatial model of the information of interest is approximated as a Gaussian process (GP), which is learned online from in-situ sensor data. The planner uses a two-layer model predictive control (MPC) scheme on a low-fidelity model of the vehicle for exploration and exploitation of the GP, subject to safety constraints. The planner trajectories are then tracked using a constant bearing based guidance law, aligning the desired orientation of the AUV toward the planned trajectory. The proposed framework enables the vehicle to plan and replan its mission as new data is obtained, while ensuring tracking of the planned trajectories and safety constraint satisfaction. Simulation results of a case study are presented for demonstrating the performance of the proposed method. An AUV is tasked with finding and tracking concentrations of marine biomass in 3D under sea ice while avoiding collisions.

Enhanced Underwater AIS for Communication-Based Collision Avoidance in Autonomous Underwater Vehicles

Data driven approach to risk management and decision support for dynamic positioning systems

Offshore oil and gas operations are inherently associated with risk and may have catastrophic consequences to life, property, and environment. Risk management is thus performed during the design, planning, and operation phases to control risk. Operational risk models are only periodically updated and do not always reflect the available real-time data. This is also the case for dynamic positioning (DP) operation. Monitoring the risk levels of the system during the operational phase could reduce the accident risk by providing additional decision support information for operators. In this paper, a framework for the risk management of DP operation is proposed to assist operators in decision-making. The risk management output will provide operators a real time risk status and pre-warnings of possible deviations in the system. This framework is developed to support the decision-making process of the operators with providing failure probability of alternative decision scenarios, and it can be applied to any other engineering system and operation. In order to validate the effectiveness of the framework, DP drilling operations are considered as a case study. The results demonstrate the value and effectiveness of the framework, which reduces the risk level of operations by contributing to the risk-informed decision-making of operators.

Risk from cyberattacks on autonomous ships

A System-Theoretic Approach to Hazard Identification of Operation with Multiple Autonomous Marine Systems (AMS)

No abstract is provided for this article.

Critical infrastructures and risk analysis of electricity supply

Exposed Aquaculture in Norway

Farming of Atlantic salmon in exposed areas poses unique challenges to operations, structures and equipment due to severe and irregular wind, wave and current conditions, and sheer remoteness. Many of the operational challenges seen at present sheltered sites are likely to be amplified when moving production to more exposed locations. There is, however, a strong Norwegian industrial interest in utilizing such areas. A new research center, the Exposed Aquaculture Operations center has been initialized to develop competence and technology to address the challenges. Six core research areas are identified that will be crucial to address the challenges with exposed farming, with a focus on the industrial status in Norway. Four areas target technological innovations that will enable safe and reliable exposed aquaculture operations: 1) Autonomous systems and technologies for remote operations, 2) Monitoring and operational decision support, 3) Structures for exposed locations and 4) Vessel design for exposed operations. Two areas represent core requirements for sustainable production: 5) Safety and risk management and 6) Fish behavior and welfare. This paper describes the research needs and the research strategy planned for the Exposed Aquaculture Operations center.

Dynamic Risk Assessment to Assist Decision-Making in Emergency Situations

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.

Adapting High Reliability Management Framework for Enhancing Resilience in Autonomous Ships

Assessment of accident theories for major accidents focusing on the MV SEWOL disaster: Similarities, differences, and discussion for a combined approach

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.

Developing safety indicators for preventing offshore oil and gas deepwater drilling blowouts

An important question with respect to the Macondo blowout is whether the accident is a symptom of systemic safety problems in the deepwater drilling industry. An answer to such a question is hard to obtain unless the risk level of the oil and gas (O&G) industry is monitored and evaluated over time. This article presents information and indicators from the Risk Level Project (RNNP) in the Norwegian O&G industry related to safety climate, barriers and undesired incidents, and discusses the relevance for deepwater drilling. The main focus of the major hazard indicators in RNNP is on production installations, whereas only a limited number of incident indicators and barrier indicators are related to mobile drilling units. The number of kicks is an important indicator for the whole drilling industry, because it is an incident with the potential to cause a blowout. Currently, the development and monitoring of safety indicators in the O&G industry seems to be limited to a short list of “accepted” indicators, but there is a need for more extensive monitoring and understanding. This article suggests areas of extensions of the indicators in RNNP for drilling based on experience from the Macondo blowout. The areas are related to schedule and cost, well planning, operational aspects, well incidents, operators’ well response, operational aspects and status of safety critical equipment. Indicators are suggested for some of the areas. For other areas, more research is needed to identify the indicators and their relevance and validity.