Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills
  1. Outputs

A wear model for assessing the reliability of cylinder liners in marine diesel engines

Academic Article
Publication Date:
2007
abstract:
Stringent in-service and operating requirements oblige marine diesel engines to possess high levels of reliability and availability. To achieve the desired reliability and availability levels, it is necessary to carry out costly maintenance activity on the cylinder liners, the wear on which is a major factor in causing diesel engine failure. This paper presents a s-based methodology that can be used to carry out a condition-based estimate of liner reliability with respect to failure through excessive wear, and to plan condition-based maintenance activity. The wear process is described through an ad hoc cumulative damage model, and the reliability of the liners is estimated on the basis of wear measures, and predictions of wear increase. The approach proposed allows reducing maintenance costs without noticeably affecting liner reliability. In fact, it gives the probability of inspecting or replacing liners only when there is a high likelihood that their wear level will exceed the wear limit before the next inspection date. The proposed model, and planning procedure have been applied to a data set consisting of wear measures of the cylinder liners of two SULZER RTA 58 engines equipping twin ships of the Grimaldi Group.
Iris type:
01.01 Articolo in rivista
Keywords:
Reliability; Wear; Naval engines
List of contributors:
Guida, Maurizio; Pulcini, Gianpaolo
Handle:
https://iris.cnr.it/handle/20.500.14243/38156
Published in:
IEEE TRANSACTIONS ON RELIABILITY
Journal
  • Overview

Overview

URL

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4118430
  • Use of cookies

Powered by VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)