Bayesian network modelling of an offshore electrical generation system for applications within an asset integrity case for normally unattended offshore installations

This article proposes the initial stages of the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore system. The main focus is the construction of a Bayesian network model that demonstrates the interactions of multiple offshore safety critical elements to analyse asset integrity. The majority of the data required to complete the Bayesian network was gathered from various databases and past risk assessment experiments and projects. However, where data were incomplete or non-existent, expert judgement was applied through pairwise comparison, analytical hierarchy process and a symmetric method to fill these data gaps and to complete larger conditional probability tables. A normally unattended installation–Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed to ensure safe operations in situations whereby physical human-to-machine interaction is not occurring. The Integrity Case can be said to be dynamic as it shall be continually updated for an installation as the quantitative risk analysis data are recorded. This allows for the integrity of the various systems and components of an offshore installation to be continually monitored. The Bayesian network allows cause and effect relationships to be modelled through clear graphical representation. The model accommodates for continual updating of failure data

Ship/Platform Collision Incident Database (2015) for offshore oil and gas installations

There is a potential for major structural damage to offshore installations leading to fatalities and serious injuries in the event of collision by either a passing or an in-field seagoing vessel. Both categories of collision have occurred on the UK Continental Shelf although to date only significant, rather than catastrophic, consequences have occurred. Internationally, collisions have occurred that have caused both loss of life and environmental damage. This report describes work to update the Ship/Platform Collision Incident Database for the UK Continental Shelf (UKCS) and the collision frequency analysis which was previously described in Research Report RR053 (2001). Report RR1153 considers collision threat detection. Data was collected from collision incident record sources to confirm or complete previous records and to expand the database up to December 2015. The database overlaps with the previous version by providing information from 1996 to 2015. The database of operating experience has been recompiled and extended to encompass all mobile and fixed installations operating on the UKCS and takes into account recent abandonments. The main database includes actual collisions, while ‘near misses’ are analysed in a separate section. In an attempt to expand the previous database and gain further understanding of the scale and nature of the ‘near miss’ events, data from a variety of sources is included: the findings are interpreted in section 4 of the report

Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV)

With the increase in the carrying capacity of passenger vessels parallel to technological developments over the last 25 years, accidents resulting in loss of lives have increased. Thus, accidents involving passenger vessels have become a major issue of concern in the maritime industry. In this study, 70 ship collision & contact accidents involving passenger vessels between 1991 and 2015 were examined. Unlike other studies in the literature, this investigation proposes a customized Human Factors Analysis and Classification System for Passenger Vessel Accidents (HFACS-PV) to facilitate analysing the human factor in passenger vessel accidents. In addition to the core HFACS structure, an additional operational condition level has been defined. The violations framework has been divided into the three sub-categories of rule violations, procedure violations, and abuse of authority, instead of the two broad categories of routine and exceptional violations. Abuse of authority is an intentional violation made knowingly and wilfully, therefore, abuse of authority has been considered separately. Furthermore, appropriate modifications have been made to the headings under the second level of HFACS-Preconditions for Unsafe Acts for compliance with the maritime industry. © 2018 Elsevier Lt

An investigation and statistical analysis into the incidents and failures associated with dynamic positioning systems

This investigation highlights the safety of DP (Dynamic Positioning) systems by briefly discussing the existing risk as-sessment methods and risk control measures provided by the International Maritime Organization (IMO). Following a study regarding DP systems and the loss of position incidents reported to the International Marine Contractors As-sociation (IMCA) and contained in the World Offshore Accident Databank (WOAD), the relevant hazards are identi-fied and collated. The primary causes of loss of position incidents were found to be: the positional reference system failures and thruster failures, with both contributing to 20.6% each of the total incidents from 2000 to 2016. Similarly the time period of the analysis is the 17 years between 2000 and 2016. Given the two stated primary causes, the po-sitional reference system failures and thruster failures, the thruster failures are analysed further. This is due to thruster failures occurring at an increased rate within DP incidents from 2011 to 2016. Hence, this trend, between 2011 and 2016, is analysed further. Three undesired events for loss of position due to thruster failures were identi-fied, these are as follows: “drive off”, “drift off” and “time loss”. Further investigation of incidents in more recent years, in this case, 2012 to 2016, identified that the DP control system accounted for 33.7% of the initiating incidents that led to thruster failures. Furthermore, the undesired event “time loss” accounted for 71.2% of total incidents caused by thruster failures

Exercise intervention in people with cancer undergoing adjuvant cancer treatment following surgery: a systematic review

BackgroundRemaining physically active during and after cancer treatment is known to improve associated adverse effects, improve overall survival and reduce the probability of relapse. This systematic review addresses the question: is an exercise training programme beneficial in people with cancer undergoing adjuvant cancer treatment following surgery.MethodsA systematic database search of Embase, Ovid, Medline without Revisions, SPORTDiscus, Web of Science, Cochrane Library and ClinicalTrials.gov for any randomised controlled trials (RCT) or non-RCT addressing the effect of an exercise training programme in those having adjuvant cancer treatment following surgery was conducted.ResultsThe database search yielded 6489 candidate abstracts of which 94 references included the required terms. A total of 17 articles were included in this review. Exercise training is safe and feasible in the adjuvant setting and furthermore may improve measures of physical fitness and health related quality of life (HRQoL).ConclusionThis is the first systematic review on exercise training interventions in people with cancer undergoing adjuvant cancer treatment following surgery. Due to the lack of adequately powered RCTs in this area, it remains unclear whether exercise training in this context improves clinical outcomes other physical fitness and HRQoL. It remains unclear what is the optimal timing of initiation of an exercise programme and what are the best combinations of elements within an exercise training programme to optimise training efficacy. Furthermore, it is unclear if initiating such exercise programmes at cancer diagnosis may have a long-lasting effect on physically activity throughout the subsequent life course

Development of an Algorithm to Convert Linear Belief Function inputs to Exponential Conditional Probability Functions for Multiple Method Applications

Evidential Reasoning (ER), based on the Dempster-Schafer theory of evidence, and Bayesian Networks (BN) are two distinct theories and methodologies for modelling and reasoning with data regarding propositions in uncertain domains. Both ER and BNs incorporate graphical representations and quantitative approaches of uncertainty. BNs are probability models consisting of a directed acyclic graph, which represents conditional independence assumptions in the joint probability distribution. Whereas ER graphically describes knowledge through an evaluation hierarchy and the relationships of the attributes based on Dempster-Shafer theory of belief functions. Therefore, this paper proposes an algorithm, which allows for the conversion of the linear input data of ER (belief degrees and relative weights) to the exponential data input of BNs (conditional probability tables (CPTs)). The algorithm is applied to a validated case study where the ER approach has been utilized for decision-makin