Universal Journal of Mechanical Engineering 3(2): 57-62, 2015 http://www.hrpub.org DOI: 10.13189/ujme.2015.030205 Reliability Ananlysis of Cooling System of Diesel Engine Dhananjay R Dolas * , Sudhir Deshmukh MGM’s Jawaharlal Nehru Engineering College, India Copyright © 2015 Horizon Research Publishing All rights reserved. Abstract Diesel engine is use in various field for the different application, the performance of engine is depends on the engine systems & components. The cooling system is one of the important systems for diesel engine, the major breakdowns are occurring due failure cooling system & its components. This paper is presented the reliability analysis of cooling system of diesel engine using for compressor application, this work is using a time to failure data of cooling system and two parameter Weibull distribution analytical least square method & Minitab 16.1R Software are used for parameter estimation, The results are shows reliability, Availability, Mean time between failure, Failure rate and Failure density. This is helpful for designing & manufacturing of components and modification. Keywords Cooling, Diesel Engine, Reliability & Weibull Distribution 1. Introduction Reliability is defined as the probability that a component or system will perform its required function for a given period of time when used under stated operating conditions [1] A cooling system is constituted by a number of components and subsystems designed to achieve a common specific result with an acceptable level of reliability. The type of component failure and its frequency has a direct effect on the system’s reliability. Thus it becomes very important to locate the critical components and analyze their reliability. Further-more, in many situations it is easier and less expensive to test components / subsystems rather than entire system. The main parts of the engine cooling system are the radiator, pressure cap, hoses, thermostat, water pump, oil cooler, heat exchanger, fan, and fan belt & pulley. The system is filled with coolant water. No matter where you live or how hot or cold the weather becomes, the mixture should be maintained the year around. The two parameter Weibull distribution requires characteristic life (η) and shape factor (β) values. Beta (β) determines the shape of the distribution. If β is greater than 1, the failure rate is increasing. If β is less than 1, the failure rate is decreasing. If β is equal to 1, the failure rate is constant. There are several ways to check whether data follows a Weibull distribution, the best choice is to use a Weibull analysis software product. If such a tool is not available, data can be manually plotted on a Weibull probability plot to determine if it follows a straight line. A straight line on the probability plot indicates that the data is following a Weibull distribution. Weibull shape parameter β also indicates whether the failure rate is constant or increasing or decreasing if β = 1.0, β > 1.0, β < 1.0 respectively. G.R Pasha & et al.[2] presented the comprehensive analysis for complete failure data. Using the Weibull Distribution for failure data &. Median rank regression (MRR) for data- fitting method is described and goodness-of-fit using correlation coefficient is applied. Sang-Jun Park & et al. [3] demonstrate a reliability goal of the pump motor assembly within an affordable amount of time and in an economic way. Using the accelerated test (AT) and measured failure rate and MTTF. Y. Lei [4] estimating the Weibull distribution parameters, three methods namely maximum likelihood estimation method (MLE), method of moment (MOM) and least-squares regression method (LSM)] and evaluated on the basis of the mean square error (MSE) and sample size. Ahmad Mahir Razali & et al. [5] compare between three methods for estimating the parameters of Weibull distributions. These methods are; moments, maximum likelihood and least squares 3-parameter Weibull distribution, using the means square error, MSE and total deviation, TD as measurement for the comparison between these methods and suggested the moments method is the best method for estimating the parameters of the 2-parameter and 3-parameter. Ghassan M. Tashtousha & et al. [6] developed the statistical model and evaluate the effect of corrective and preventive maintenance schemes on car performance in the presence of system failure where the scheduling objective is to minimize schedule duration. Sunil Dutta & et al. [7] carried out reliability analysis defense vehicle gear box assembly using the Weibull distribution. Aurelian Constantin & et al. [9] presented the reliability analysis of automobile shock absorbers using the Weibull ++7 program. Yunn-Kuang Chu [8] examines the estimation comparison of two methods for Weibull parameters, one is the maximum likelihood method and the other is the least squares method. Based on sample