Operations at sea-based fish farms can be challenging, and several risk dimensions are of concern during operations. Sea lice represent a challenge for the fish farmers who are required to perform delousing when the infestation levels rice above a set value. Delousing operations are frequently performed and require the use of heavy machinery operated from service vessels moored to the net-cages. Operators are exposed to hazards that may cause severe injuries and fatalities. Escape of salmon, which is a substantial environmental risk, has occurred in relation to delousing operations. Chemicals used during the operations may cause negative environmental consequences. Other safety related issues are the fish health and welfare. In this paper, a delousing operation on a fish farm is discussed with respect to different dimensions of risk, and potential conflicting objectives are discussed.
In recent years, International Maritime Organization (IMO) and the classification societies have issued a number of regulations and guidelines addressing both environmental performance and human safety of ships. All of these regulations and guidelines are aimed at building improved vessels, but environmental performance and human safety are usually considered separately. Sometimes, this results in the two issues conflicting with each other: a regulation that is intended to improve environmental performance may have negative effects on human safety and vice versa. The objective of this paper is to investigate the conflict and points towards the need for an integrated evaluation of both environmental performance and human safety of ships. As a basis, this paper provides several examples in which environmental performance and safety contradicts each other. Based on the examples, a conceptual description of the problem is provided, including suggestions for further work.
Norwegian fish farms are expanding into more exposed sites due to a lack of sheltered coastal locations and growing negative ecological consequences close to land. The severe wave and current conditions, irregular and often high winds, sheer remoteness, and limited weather window amplify the hazards during aquaculture operations. This paper presents a methodology for identifying hazards in aquaculture operations that take into account risks to personnel, material assets, the environment, fish welfare and food safety. The methodology considers the marine fish farm, its support service systems, external agents impacts, potential environmental disturbance and the likelihood of hazard interactions, and fish in the cage. The methodology has been used to analyse a net-cleaning operation, and the results show that it provides a good overview of hazards in a specific operation. The operator can have a better understanding of the nature of the operation to be better prepared for what can go wrong. The paper concludes that the risk assessment framework for aquaculture operation needs to be holistic to take into account multiple dimensions of risk, and the proposed methodology serves a good basis to develop the framework further. The paper also brings up the necessity to investigate further the definition of major accident and major accident hazards in aquaculture, especially for exposed fish farms that are exposed to different hazards comparing to the coastal fish farms today.
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The Safe Return to Port (SRtP) regulation requires physically separated redundancy for passenger ships to ensure the safe return of the ship after a casualty. Although complying with this regulation significantly increases shipbuilding time and cost, the quantitative effect of this regulation on system reliability is not well known. The main objective of this article is to investigate the SRtP regulation by reliability analysis methods and show the effect of this regulation through case studies. This article suggests reliability analysis methods for two different modes of ship system operation; continuous mode of operation and low-demand mode of operation. Location fault tree and a Markov model are suggested, respectively. Two case studies focusing on fire and flooding for each mode of operation demonstrate that applying the SRtP regulation increases the system reliability substantially.
This chapter discusses human factorHuman factor effects on the system risk levelRisk level and presents related quantification modelModel in this field. Human errors, response timeResponse times modelsModel, and methods for human factorsHuman factor analysis and quantification are discussed. First, background research on human factorHuman factor quantifications in risk assessmentsRisk assessment is presented (Sect. 5.1). Then, a general overview on the role of human operators and their effect on system failureFailure is discussed (Sect. 5.2). In Sect. 5.3, performance shaping factorsPerformance shaping factors (PSFs) that affect human behaviorHuman behavior are identified. Generally, human error quantification methods could be classified into two main categories: These two categories are presented and discussed in Sects. 5.4 and 5.5, respectively. Lastly, in Sect. 5.6, human response timeHuman response time modelsModel are presented. These modelsModel provide a better understanding on the timeline of the decision-makingDecision-making process of operators in emergencies that affect system risk levelRisk level significantly.
Since few or no human operators are directly involved in the operation of an autonomous marine system (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision making. The current study combines the system-theoretic process analysis (STPA) with Bayesian belief networks (BBNs) to develop online risk models for an AMS. Furthermore, fuzzy discretization is introduced to deal with evidence uncertainty. The proposed risk model can update the risk level as the operating conditions change, providing a basis for AMS supervisory risk control (SRC). A two-level SRC is proposed in this study. Using the operation of an autonomous underwater vehicle (AUV) under sea ice as an example, the current work presents an online risk model and a corresponding SRC system, focusing on the navigation hazards to the AUV and its potential loss. The results of simulation studies show that the model enables the AUV to be informed of the risk level and to make risk-based decisions accordingly, thereby improving its intelligence. The importance of the evidence uncertainty in online risk models and SRC is analyzed and discussed. The results and conclusions of this analysis can be adapted to other AMSs.
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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.
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