Review of the safety engineering techniques for a complex ship system

Marine industry is leaning towards the autonomous vessels; and advanced technologies are being developed for autonomous operations. However, this rapid technological change has increased the level of complexity in ship systems. As the interactions between components are increasing further and software are getting imbedded into components, the nature of risks in modern systems can be different than in the traditional systems; where the risks were mostly limited to human errors and component failures. However, for identifying risks in modern systems, it is first important to understand the system composition and the behavior of components. Since traditional system-safety engineering techniques, developed for the relatively simpler systems in past, are still dominant in marine industry. These techniques may not be able to cope with the risks due to increasing complexity.This paper reviews and identifies a suitable modelling approach and a risk analysis method for a complex ship system. A modern modeling approach known as Systems-Modeling Language (SysML) and a modern risk analysis method known as Systems-Theoretical Process Analysis (STPA) are reviewed and compared with widely used traditional methods known as the Tree structure method and Fault Tree Analysis. SysML is a graphical modeling language that presents structural composition, component functions, behavior, constraints and requirements of a complex system. STPA is a risk analysis method that aims to identify and mitigate risks in a complex system. The review and comparison results are presented in the paper.The results of this study suggest that the modern methods are more suitable than the traditional methods when the functionality of each method are considered. However, as the modern methods are more detailed, and are focused on the functionality, they are relatively complex and require more resources for the analysis in comparison to the traditional methods. Some viable solutions to improve the drawbacks of SysML and STPA, and possible future research topics are presented.Peer reviewe

Developing fuzzy logic strength of evidence index and application in Bayesian networks for system risk management

Digitalization is becoming a trend in our modern society and systems. Meanwhile, risk analysis and management has rooted and been applied in various fields. Therefore, there is an increasing need to integrate risk analysis and management into the coming digital society. Risk has been represented digitally by the product of probability and consequence i.e. R = P x C traditionally. However, it has been increasingly discussed to include strength of evidence (SoE) in addition to the traditional consequence (C) and probability (P). Although much advance has been achieved along this direction, there still remains challenges, e.g. ambiguity in rating SoE and visual expression of risk diagrams. This paper focuses on addressing these issues and meanwhile aims to make the risk expression fully digital so that it is more efficient and flexible to be included in a system analysis and visualization. This is achieved firstly by reviewing state-of-the-art discussions on SoE assessment in risk management and identifying the remaining challenges. Then, the paper proposes an approach to address the challenges by forming a fuzzy logic SoE index based on fuzzy logic theory, which enables a transfer from linguistic variable to a digital one with the ambiguity avoided. After the SoE index is formed, it is applied into BNs as the node size index to demonstrate its practical application. Meanwhile, with the BNs forming the infrastructure to calculate and present consequences and probabilities, it showcases a new system risk management approach. All the variables in the system can be expressed in a risk diagram. This further enables an improved risk visualization, risk management and risk communication for system analysis, towards risk digitalization.Peer reviewe

Hazard Identification in Winter Navigation

In this report, a hazard identification in winter navigation has been performed in order to detect, determine, list and categorize different relevant hazards threatening the safety performance and development of the winter navigation operations. The analysis presented in this report aims to gather all available information from different sources in order to detect hazards for the practice of winter navigation, having a particular consideration of winter navigation hazards of oil tankers operation and/or vessels with similar characteristics. The report introduces the implemented framework to detect the relevant hazards of winter navigation and describes in details the three utilized sources to detect those hazards: hazard identification workshops with winter navigation experts, accident cases analysis, and analysis of accident statistics of 5 winters. Results obtained are presented after the performed collection and data analysis. Finally, discussion and conclusions are drawn

On reliability assessment of ship machinery system in different autonomy degree; A Bayesian-based approach

Analyzing the reliability of autonomous ships has recently attracted attention mainly due to epistemic uncertainty (lack of knowledge) integrated with automatic operations in the maritime sector. The advent of new random failures with unrecognized failure patterns in autonomous ship operations requires a comprehensive reliability assessment specifically aiming at estimating the time in which the ship can be trusted to be left unattended. While the reliability concept is touched upon well through the literature, the operational trustworthiness needs more elaboration to be established for system safety, especially within the maritime sector. Accordingly, in this paper, a probabilistic approach has been established to estimate the trusted operational time of the ship machinery system through different autonomy degrees. The uncertainty associated with ship operation has been quantified using Markov Chain Monte-Carlo simulation from likelihood function in Bayesian inference. To verify the developed framework, a practical example of a machinery plant used in typical short sea merchant ships is taken into account. This study can be exploited by asset managers to estimate the time in which the ship can be left unattended. Keywords: reliability estimation, Bayesian inference, autonomous ship, uncertainty.</p

A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships

Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.</p

Prognostic health management of repairable ship systems through different autonomy degree; From current condition to fully autonomous ship

Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.</p

Hazard analysis process for autonomous vessels

This report introduces a systemic process for an initial hazard analysis in the operative context of autonomous vessels. The process facilitates executing an initial analysis of safety hazards in the earliest design phase before the planning of ship design, materials, structures, components, systems and the services linked to the functioning of an autonomous vessel. The process attempts to produce information to make the systematic integration of safety controls that need to be implemented in an initial safety management strategy. In this report, the process is applied to analyse the safety hazards in the foreseen functioning of two concepts of autonomous ferries operating in urban waterways in and near the city of Turku in Finland. The process first identifies the main type of accidents and hazards in the operational context of these ferries. It then proposes high‐level safety controls to mitigate the hazards and prevent these accidents. The controls are subsequently used as a basis for developing an initial safety management strategy for autonomous ferries and their operational system. This provides a systematic representation of safety controls in the operative context of autonomous ferries. The full process is composed of five different steps to elaborate a systematic analysis of hazards and to define safety controls for mitigating and preventing the identified hazards. These controls are the basis of the initial safety management strategy of autonomous vessels and their operational system. This report was done as part of the ÄlyVESI – Smart City Ferries research, development and innovation project. Smart City Ferries, the ÄlyVESI project, was a conceptualisation, product development and innovation project realised by cities, businesses and universities 1.10.2016 – 31.5.2018. The project explored, developed and tested new technologies and intelligent urban waterborne traffic solutions and services. Novia University of Applied Sciences, Turku University of Applied Sciences, Aalto University and the City of Turku carried out the project in co‐operation. The project was funded by the 6Aika‐program of the European Regional Development Fund. In addition, the project was funded by the Finnish Transport Safety Agency and the cities of Helsinki and Espoo

Bayesian Network Model of Maritime Safety Management

Maritime traffic operations encompass several activities that, due to their general characteristics, compromise the safety of the personnel at sea and ashore. Maritime safety management and its derived standards have constantly reviewed, evaluated and improved the general conditions of the mentioned operations. Nevertheless, the correct use and application of maritime safety management standards will always depend on the particular interpretation by maritime safety experts. Thus, the combination of maritime safety standards and the knowledge of maritime experts is and will always be an important factor that influences the course of safety management in maritime traffic operations. This thesis provides a new proposal for the modeling of maritime safety management: the Bayesian Network Model of Maritime Safety Management. The model integrates the most relevant components of maritime safety management within the content of maritime safety management standards, the interpretation of safety management by maritime safety experts, and several practical indicators of the maritime safety performance. In order to model the safety management in maritime traffic, this study has adopted Bayesian networks methodology due to its remarkable characteristics for modeling uncertain expert knowledge. Furthermore, the proposed model has also been tested through a practical application in the local shipping industry. The aims during this test were to analyze dependencies and logic of the components of the model, and also to collect expert knowledge and information regarding to current maritime safety management practices. Thus, the obtained results in this thesis propose and evaluate the possibility of using Bayesian networks as a tool to model and analyze the safety management of maritime traffic operations

Maritime risk and safety management with focus on winter navigation

Maritime transport is commonly categorized as one of the riskiest industry sectors. In Finnish sea areas, the risks increase during winter navigation when ships have to navigate in sea ice conditions. In order to support and ensure the integrity of people, ships, and environment during winter navigation, different safety and risk management strategies are developed. Risk management aims at developing methods for detecting, analysing, mitigating, and controlling the risks threatening the safety of winter navigation. Safety management aims at establishing and promoting organizational practices for planning, implementing, reviewing, controlling, and improving the safety performance of winter navigation. This thesis provides a review of the current safety performance of winter navigation in Finnish sea areas and proposes alternatives to control and improve it. To this end, the thesis first provides understanding and evidence of the risks threatening the safety of the winter navigation system. Ship collisions during ship independent navigation and ship collisions in convoy operations in medium and severe ice conditions are identified as the accidents with highest risk.These identified contexts are the basis for developing a model that analyses the risk of collision during winter navigation. The model combines the analysis of the role of humans in the execution of the operations and operational aspects of the performance of ships in ice conditions. It is used as a risk management tool that proposes risk control options and assesses their potential efficiency for supporting and improving the safety performance of winter navigation. The assessment of the risk control options points out the need for improving and simplifying safety and risk management in the planning and executing of winter navigation operations. In particular, there is a lack of coherent safety management systems that would enable practical adoption and application of regulatory demands, ensure their suitability to actual operational needs, and determine coherent safety key performance indicators. Based on this, the thesis offers a method for executing a systematic application and performance measurement of the requirements contained in maritime safety management regulations. Moreover, the thesis introduces a process for designing maritime safety management systems based on a system engineering approach and proposes a tool for reviewing safety performance.Through a case study, these proposed alternatives are combined for designing a safety management system for one of the main responsible actors controlling and ensuring the safety of ship navigation. This case study makes a representation of the advantages in the management of maritime risk and safety, with a special focus on winter navigation

The design of VTS Finland Safety Intent Specification

This report presents an integrated systems and safety engineering process describing the safety function of the Vessel Traffic Services in Finland. The process is based on the analysis of the safety intent specification of the VTS system, this analysis is guided by a structured methodology linked to the Systems-Theoretic Accident Modelling and Processes (STAMP). The process aims at systematically representing the controls utilized in VTS Finland to ensure the safety of the navigation in Finnish sea areas and representing the support provided to other maritime partners involved in the execution of navigational operations.  This analysis covers all aid services provided by VTS, and it makes particular distinctions between services provided during spring-summer-autumn and winter seasons. The process proposed in this study provides a description of the outcome of the executed analysis to key elements interacting in the VTS safety system. Thus, this covers the program management ruling the system, the system purpose and functional principles, the system architecture, the systems design and physical representation, and the actual development of the system operations.  Moreover, this process identifies and analyses the link between the functioning of the Finnish VTS safety system and the demanded regulations (guidelines) by the International Association of Lighthouse Authorities (IALA), having a special focus in the provision of training and formation to VTS personnel who are mainly responsible for ensuring the system functioning.  The constructed process culminates with the provision of a performance-monitoring tool that implements a set of determined Key Performance Indicators (KPIs) which are created to support the planning, monitoring and evaluating of the key elements interacting in the VTS safety system. These KPIs are integrated into the structure of a Bayesian network model that is utilized as a decision-support tool in the actual safety management of the VTS system