Unravelling causal factors of maritime incidents and accidents

Puisa, Romanas; Lin, Lin; Bolbot, Victor; Vassalos, Dracos

doi:10.1016/j.ssci.2018.08.001

Cited by 77 publications

(32 citation statements)

References 30 publications

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“…This observation is in line with previous studies. For example, a study performed by Puisa et al (2018) revealed that an inadequate training was observable in numerous past accidents, and it was a frequent causal factor across all reports analyzed within aforementioned study. In addition, Graziano et al (2016) failures were associated with factors like fatigue or inadequate training/instruction.…”

Section: Resultsmentioning

confidence: 99%

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Maya

Babaleye

Kurt

2019

Saf. Extreme Environ.

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Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach. Keywords Maritime accidents. Maritime safety. Maritime accident learning with fuzzy cognitive maps (MALFCMs). Human factors. Bayesian networks (BNs)

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Section: Resultsmentioning

confidence: 99%

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Maya

Babaleye

Kurt

2019

Saf. Extreme Environ.

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“…In this work, the process was augmented by a checklist, developed on the basis of previous studies (Becker & Van Eikema Hommes, 2014;Blandine, 2013). The main categories of causal factors are:  Inappropriate control input  Hardware failure  Software faulty implementation  Software faulty design  Erroneous or missing input  Inadequate control command transmission  Flawed execution due to failures in actuator or physical process  Conflicting control actions The systemic and contributory causal factors (Puisa, Lin, Bolbot, & Vassalos, 2018) have not been considered during identification of the causal factors, as the implementations of proper training for system operator and maintenance is out of the scope of system designer. The aim of the designer is to ensure the adequate reliability and availability of system functions.…”

Section: Methodsmentioning

confidence: 99%

A comparative safety assessment for Direct Current and Direct Current with hybrid supply power systems in a windfarm Service Operation Vessel using System- Theoretic Process Analysis

Bolbot¹,

Puisa²,

Theotokatos³

et al. 2020

Proceedings of the International Seminar on Safety and Security of Autonomous Vessels (ISSAV) and European STAMP Workshop and C

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As windfarms are moving further offshore, their maintenance has to be supported by the new generation Service Operation Vessels (SOV) with Dynamic Positioning capabilities. For the SOV safe operations it is crucial that any hazardous scenario is properly controlled. Whilst international regulations require the implementation of Failure Modes and Effects Analysis (FMEA) for SOV power systems, FMEA has been criticised for not addressing properly failures in control systems. In this study, System-Theoretic Process Analysis (STPA) is employed for identifying the hazardous scenarios in terms of Unsafe Control Actions (UCAs) in Direct Current (DC) and DC with batteries power systems. Then the identified UCAs are ranked based on their risk. The results demonstrate that the number of hazardous scenarios derived by the STPA increases in a power system with batteries in comparison to a conventional DC power system, thus depicting higher complexity of this system. However, the increase in overall risk is small and within acceptable limits, whilst the risk reduces for a number of UCAs leading to Diesel Generator overload sub-hazard.

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“…The finding is further demonstrated by Rollenhagen (79) in that there is a lack of attention to the organisational context in which an accident investigation is taking place, demonstrating a lack of appreciation for Categories 2 and 3. Further evidence of a limited application of systemic causal factors in accident investigation is shown by Puisa (80,81).…”

Section: Accident Analysis and Recommendationsmentioning

confidence: 99%

Analysis of effectiveness of fire safety in machinery spaces

McNay

Puisa

Vassalos

2019

Fire Safety Journal

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The primary cause of machinery space fires is cited as the release of flammable oil mist contacting unprotected hot surfaces. With this being common knowledge why do we continue to see this type of incident reoccurring on ships carrying thousands of passengers to precarious destinations? We review regulatory and Class requirements with respect to fire safety, Formal Safety Assessment (FSA) as a rule-making instrument of the IMO, accident investigation recommendations involving fire, fire safety technology, and research literature. For each constituent of fire safety control, the paper aims to determine the preferred strategies for accident prevention and their relative cost effectiveness. Subsequently, the cost effectiveness of the current safety approach is established. This review found that fire prevention could be made more cost effective if its scope was broadened beyond the detection of proximate events occurring immediately before ignition. The findings suggest that more cost-effective safety measures can be developed by addressing systemic causes of fire. Removal of the systemic causal factors of fire from the equation, means we can address fire safety prior to the moments before ignition when the point of no return can unwittingly be passed. The paper suggests directions of how this can be achieved. Design & Operation Implementation of design and operational safety measures Rules & regulations (existing and prospective-FSA) Political, commercial and other pressures Accident investigation Safety systems on market from technology suppliers Knowledge base Academic research

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Unravelling causal factors of maritime incidents and accidents

Cited by 77 publications

References 30 publications

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

A comparative safety assessment for Direct Current and Direct Current with hybrid supply power systems in a windfarm Service Operation Vessel using System- Theoretic Process Analysis

Analysis of effectiveness of fire safety in machinery spaces

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