Improving ship maintenance : a criticality and reliability approach

Ship maintenance has evolved through the years incorporating tools and techniques already applied in other industrial sectors. The obvious benefits from such an application include improved safety, environmental protection, asset integrity, minimisation of downtime and increased operability. In this paper, a predictive maintenance approach is described employing reliability and criticality analysis tools. Its application on the Diesel Generator (DG) system of a motor cruise ship is also presented. Well known tools such as Failure Modes, Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) using static and dynamic gates together with reliability Importance Measures (IMs) are applied. The results of this research paper include the estimation of the reliability of the main system and sub-systems and the identification of their critical components as well as suggesting measures in order to prevent and/or mitigate the failures of the under-performing equipment

Increasing ship operational reliability through the implementation of a holistic maintenance management strategy

Ship maintenance was initially considered as more of a financial burden than as a way to preserve safety, environment and quality transportation. The benefits from applying a sound and systematic maintenance policy are emerging both in the minimisation of unnecessary downtime as well as in the increase of operational capability. In this paper, a novel predictive maintenance strategy is demonstrated, combining the existing ship operational and maintenance tasks with the advances stemming from new applied techniques. The initial step for the application of the above-mentioned strategy is also shown regarding the machinery space of a cruise ship. Well-known tools are applied such as Failure Modes, Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA). Outcomes of this study are the identification of the critical components of the system, the estimation of the reliability of the overall system and sub-systems, the prioritisation of the maintenance tasks and finally the availability of the specific end events/items

Parametric rolling behaviour of azimuthing propulsion-driven ships

The study investigates the experimental and numerical analysis of the occurrence of auto-parametric rolling for large, high-speed pod-driven ships in waves. Considering unique design and performance targets, the aim here is to exploit susceptibility to auto-parametric rolling behaviour and to identify probable design and operational precautions. In order to achieve this aim, an existing non-linear timedomain software to simulate capsizing and other critical manoeuvring behaviours of slow- to mediumspeed conventional and podded ships in waves is being enhanced for fast pod-driven vessels and then compared against the dedicated model test conducted in long-crested regular and random waves for a large, pod-driven containership model. This paper includes the presentation of current numerical modifications for pod-driven ships and the verification analysis

RECENT ADVANCES ON QUASI-STATIC RESPONSE OF SHIP AND OFFSHORE STRUCTURES

ABSTRACT This paper presents a summary of the recent advances on Quasi-static response of ship and offshore structures as discussed by the Technical Committee I

New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice