International Journal of Reliability, Quality and Safety Engineering

In this paper stochastic relations and closure results for weighted and transformed distributions including the proportional hazards model are presented. Exponential approximations to the class of increasing failure rate (IFR) and decreasing failure rate (DFR) weighted distributions including transformed distributions with monotone weight functions are obtained. These include approximations via the proportional hazards and length-biased exponential distributions. Also, bounds and moment-type inequalities for weighted life distributions including proportional hazards models and some applications are presented.

In this article some properties of the mean time to failure in an age replacement model is presented by examining the relationship it has with hazard (reversed hazard) rate and mean (reversed mean) residual life functions. An ordering based on mean time to failure is used to examine its implications with other stochastic orders.

This paper proposes a random and age replacement policy for an operating unit which works at random times. First, the unit is replaced before failure at a planned time T or at the completion of a working time, whichever occurs first. The expected cost rate is obtained. Next, as one extended model, the unit is replaced before failure at a number N of working times or at a planned time T. An optimal policy which minimizes the expected cost rate is discussed analytically, and its numerical example is given. Two modified models, where the unit is replaced at the first completion of the working time over time T or at number N, and it is replaced at time T or number N, whichever occurs last, are considered. Furthermore, we show that this corresponds to a cumulative damage model by replacing shock with work. Finally, one optimization problem of how much time to preset for scheduling the completion of N works is proposed.

The present paper deals with the opportunities for the availability predictive modeling of a thermal plant using Markov process and probabilistic approach. These opportunities will be identified by evaluation of a predictive model to be built for the steam generation system of a thermal power plant. This feasibility study covers two areas: development of a predictive model and evaluation of performance with the help of developed model. The present system under study consists of five subsystems with three feasible states: full working, reduced capacity working and failed. After drawing transition diagram, differential equations are generated and then a probabilistic simulated predictive model using Markov approach has been developed considering some assumptions. Availability matrix for each subsystem is also developed, which provide various availability levels. On the basis of this study, performance of each subsystem of steam generation system is evaluated and then maintenance decisions are made for subsystems.

The complexity of modern manufacturing underscores the need for improved control strategies for these systems. We formulate a Bayesian attribute sequential monitoring plan useful for adaptive control of a production process. The Bayesian framework is based on natural informative conjugate priors that provide an optimal adjustment interval k in response to real-time changes in the operating characteristics of a process. The sampling and adjustment decisions are based on the tradeoffs between quality costs and quality parameters resulting from the process. The parameters include the fraction of nonconforming products p, and the number of nonconforming products r. The quality costs, which include the inspection, cost, repair cost, nonconformance cost, and adjustment cost are fixed and used to develop a breakeven relationship that that results in a sequential sampling construct that optimizes both the systems costs and process quality.