No abstract is provided for this article.
No abstract is provided for this article.
The process applied for verification of maritime systems lacks the ability to properly examine complex networks of interconnections. Verification is mainly focused on single failures of components, not properly accounting for the complexity emerging through interactions between human operators, computer systems and electro-mechanical components. The problem apparently resides in the supporting studies, or the lack thereof, for the development of test cases. A new methodology that can be introduced to the current verification process for these systems is proposed in this article. It employs Systems-theoretic process analysis (STPA) to generate verification objectives and related hazardous scenarios. These specify or extend the scope and provide acceptance criteria for verification activities, and may further serve as input to test case generation. The method is used in a case study to identify verification objectives for an automated module in the power management system of a maritime vessel. The results show that the method is able to reduce the number of context variables that verification results depend upon, and to highlight remaining context dependency, to allow for an integrated system view. It will help capture accidental scenarios with more complex causal relations than what is currently considered during verification of these systems.
Unmanned autonomous cargo ships may change the maritime industry, but there are issues regarding reliability and maintenance of machinery equipment that are yet to be solved. This article examines the applicability of the Reliability Centred Maintenance (RCM) method for assessing maintenance needs and reliability issues on unmanned cargo ships. The analysis shows that the RCM method is generally applicable to the examination of reliability and maintenance issues on unmanned ships, but there are also important limitations. The RCM method lacks a systematic process for evaluating the effects of preventive versus corrective maintenance measures. The method also lacks a procedure to ensure that the effect of the length of the unmanned voyage in the development of potential failures in machinery systems is included. Amendments to the RCM method are proposed to address these limitations, and the amended method is used to analyse a machinery system for two operational situations: one where the vessel is conventionally manned and one where it is unmanned. There are minor differences in the probability of failures between manned and unmanned operation, but the major challenge relating to risk and reliability of unmanned cargo ships is the severely restricted possibilities for performing corrective maintenance actions at sea.
The objective of this paper is to develop online risk models that can be updated as conditions change, using risk as one metric to control an autonomous ship in operation. This paper extends and integrates the System Theoretic Process Analysis (STPA) and Bayesian Belief Networks (BBN) with control systems for autonomous ships to enable supervisory risk control. The risk metric is used in a Supervisory Risk Controller (SRC) that considers both risk and operational costs when making decisions. This enables the control system to make better and more informed decisions than existing ship control systems. The novel control system is tested in a case study where the SRC can change: (i) which machinery system is active; (ii) which control mode to run the ship in; and (iii) which speed reference to follow. The SRC is able to choose the optimum machinery, control mode, and speed reference to maintain safe control of the ship over a route in changing conditions.
Risk assessment (i.e., risk analysis and risk evaluation) is routinely performed in marine operations by vessel crews to inform and support the decision-making process for the ship’s operations. With the inevitable ubiquity of autonomous marine vehicles, the need for effective autonomous decision-making methods which considers risk factors increases, to enable these vessels to perform at least as safely and reliably as manned ones. This paper presents an initial non-exhaustive systematic review of the literature on risk-based decision-making methods for Autonomous Marine Vehicles. Specifically, we are interested in the intersections between risk science, artificial intelligence, and autonomous marine vehicles, and in defining the state-of-the-art in this specific scope. We look at the last five years of the literature using the snowballing systematic review technique, selecting 23 papers after one iteration. After data extraction on the 23 papers, we compile the most common methods used in implementing risk-based decision-making in autonomous marine vehicles, and discuss the state-of-the-art.
This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managing the operational risks involved. Although human supervisors, e.g., located in remote operating control centers are still needed for safety and liability reasons, there is a growing demand for complex decisions to be made by the onboard control system itself, both during normal operations and in emergencies. This paper suggests general principles for how the results from systems-theoretic process analysis (STPA) can be transformed into a quantitative and computationally tractable optimization problem, solved by a MPC-based decision-making algorithm for autonomous navigation. The proposed method is demonstrated and evaluated by simulating an autonomous ship navigating in a coastal environment. It is concluded that the proposed method may serve as a reasonable and valuable bridge between the realms of qualitative risk analysis and numerical optimal control for risk-aware autonomous control and decision-making.
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.
Petroleum exploration and production in the Barents Sea is a controversial topic. The Goliat field outside the northern coast of Norway will be the first offshore oil development in this region, with planned production start in 2013–2014. Avoiding major accidents at Goliat is critical; not only to reduce the risks to human lives and the environment, but also to gain political acceptance. Providing early warnings of major accidents for Goliat is one of the main objectives of the research project ‘Building Safety’. The objective of this paper is to describe the development of early warnings in the form of indicators. In addition, the paper includes an overview of current status of early warnings of accidents in other major hazard industries; the nuclear power industry, the chemical process industry, and aviation. Experiences from these industries, including lessons learned from recent major accidents, have been used as important input to the development of early warning indicators.
Autonomous marine systems may switch between various operational modes with different levels of autonomy (LoA), due to a rapidly changing environment and the complex nature of tasks. The dynamic autonomy brings an additional layer of complexity to ensuring safe marine operations, but this functionality is not sufficiently considered in current risk analysis methods. Hence, this paper proposes an approach to hazard identification based on the system theoretic process analysis (STPA) that includes unsafe transitions between different LoA in systems. A case study of a remotely operated vehicle (ROV) with four operational modes with different LoAs is used to illustrate the approach. The results show that the proposed approach contributes to: 1) communicating a shift of responsibilities among human operator and system controller in different operational modes by specifying how the allocation of the responsibility between human operators and the controller changes, and what updated process model of the operator and the controller are to ensure a successful transition; 2) refining safety constraints to be more concrete to improve system design, and operational procedures and 3) identifying triggering events for marine system modes' transitions to handle environmental interaction systematically and sufficiently.
With increasing autonomy in systems, the role of software becomes more prominent as it overtakes human operator functions. The software in autonomy differs from automation with respect to functionality, implementation, and complexity, and software failures contribute to system and operational risk. Such failures, however, are often not sufficiently catered for in current risk assessments and mitigation processes, as they are challenging to identify and quantify, in particular, in the early conceptual design phase. Software reliability is not the same as software safety, as the latter encompasses the context and use of the software, as well as interactions and potential cascading failures to hardware, humans, and the environment. It is also difficult to investigate cascading effects on the system that may follow from software failures. The objective of this paper is to propose a novel classification taxonomy to support a more thorough identification of software failures for systems with different degrees of autonomy, as well as for software implementation techniques. The risk from software is interwoven into the design, development, validation, and verification processes, impacting safe operation. The proposed taxonomy can be used iteratively from the early design phase as the detailed design concepts evolve. The level of abstraction for system and software functions decreases with the design and development process. The validation and verification processes must ensure the software’s safety and reliability on different system abstraction levels. The software taxonomy in this paper includes relevant causes, consequences, and process relationships, and has been created based on existing industry classifications, research, and system models. A case study applying the taxonomy to navigation and collision avoidance functions on the subsystem level of a Maritime Autonomous Surface Ship (MASS) is performed. Software properties extracted from existing systems and knowledge are transformed into a functional model. Each software failure is then described in the context of the system level valid for the design, development, validation, and verification processes for MASS. The overall outcome of the paper may contribute to the safer design of systems through enhanced identification of potential hazards and software failures, leading to improved risk assessments and, as such, a better basis for defining more efficient safety requirements for autonomous systems from the early system development. Even though the paper exemplifies the taxonomy and classification by focusing on MASS, the work has relevance to other types of software-intensive systems.
This article presents an overview of reported injuries in the Norwegian aquaculture industry focusing on the production of Atlantic salmon and trout, which dominates the fish farming industry in Norway. Two different data sets form the basis for the analysis: (i) occupational injuries reported to the Norwegian Labor and Welfare Administration, and (ii) serious occupational injuries reported to the Norwegian Labor Inspection Authority. The data sets on occupational injuries and serious injuries provide information about mode of injury, type of injury, affected body parts, and time of year of the reported injuries. The results and the injury trends are analyzed and discussed in light of important characteristics and changes in the Norwegian fish farming industry, including underreporting. This information is useful in safety management and for allocating resources for risk-reducing measures.
