Dr. Krishnan V

In this paper, is to permit the system reliability analysts/managers/engineers/ practitioners to conduct RAM analysis of the system which may help them to model, analyze and predict the behavior of industrial systems in a more realistic and consistent manner. Markovian approach is used to model the system behavior. For carrying out study, Root Cause Analysis (RCA) of the subsystems is carried out and transition diagrams for various subsystems are drawn and differential equations associated with them are formulated. After obtaining the steady state solution the corresponding values of reliability and maintainability are estimated at different mission times. With RAM analysis of the system key performance metrics such as Mean Time between Failure (MTBF), Mean time to Repair Time (MTTR) and System availability values are ascertained.

On the basis of well known B-model a cummulative damage model is introduced. The model is based on fracture mechanics
hence named as FM-model. The objective of the paper is to relate the basic idea of B-model in such a way so as to transfer
results from one structure to another. The accounts of the geometry of structure is taken through stress intensity fator. The
model is applicable in wider sense both deterministic as well as to random loading

We study on energy homeostasis ensures the functionality of the entire organism. The human brain as a missing link in the global regulation of the complex whole body energy metabolism is subject to recent investigation. The goal of this study is to gain insight into the influence of neuronal brain activity on cerebral and peripheral energy metabolism. In particular, the tight link between brain energy supply and metabolic responses of the organism is of interest. We aim to identifying regulatory elements of the human brain in the whole body energy homeostasis.

This paper focuses on a case study to estimate the reliabilities of two competing machines, when the only available data is Time To Failure. The Weibull Parameters are calculated using Microsoft Excel 2010. The results show that after knowing the reliabilities of both the Bulldozers at different lengths of time, we can ascertain which of them is preferable to use at which time period. Study of reliabilities of machinery used in any kind of production is of utmost necessity for optim um use of man power and resources to make the process cost effective and with minimum downtime. This is applicable for all large and small industries alike. But in small industries data is not accurately stored and it becomes difficult to estimate product reliabilities.

This paper presented the estimation of system reliability using two parameter Weibull distribution. The parameters are estimated using Weibull probability plot & statistical analysis and the results being presented in charts. An increasing complexity of systems often leads to an increase of failure mechanisms. A statistical analysis of the lifetime of systems with several failure mechanisms consisting of several subcomponents considered The Weibull distribution is commonly used as a lifetime distribution in reliability applications. The two-parameter Weibull distribution can represent a decreasing, constant or increasing failure rate.

In this paper, we present the study of the two-parameter Weibull distribution theory and its parameters (shape β and scale α) using Weibull Probability Plotting. Using the failure data for haul trucks in operation at a marble quarry, we obtain the fatigue life equation by regression under different failure probabilities. Weibull distribution analysis for reliability and maintainability is showing a tendency of increasing failure rate, leaving room for decisions on reliability centered maintenance planning, machine improvements, optimal load and the need for review of data collection process.