Predicting Failures for Repairable System Subjected to Imperfect Maintenance

The purpose of this project is to develop a reliability model which results from reliability analysis conducted on repairable system subjected to imperfect maintenance. Hence, in order to perform the reliability analysis, field data from actual equipment failure were gathered and analyzed. In this project, the equipment selected was the centrifugal pump used in one of the petrochemical plants. Various stages had been conducted in order to achieve the objectives of the project. This includes data screening and analysis, determination of failure distribution as well as the maintenance effectiveness which denoted by q. All of these phases were performed by using the reliability software, Weibull ++7. The data analysis showed that the failure data displayed Weibull distribution while q value indicated the Generalized Renewal Process (GRP) is the most applicable probabilistic models that characterized the failure data. Thus, the reliability model was developed by using GRP model of Type I and Type II. The comparison between both models was conducted to select the suitable model to be used in developing the reliability model. Based on the likelihood value (LV), GRP model Type I was selected as it possessed higher LV and this model was used to predict the future failures of the system. Evaluation phase was conducted to verify that GRP model Type I was the most suitable model which fits best the failure data. In this phase, the reliability model was developed by using other probabilistic models such as Renewal Process (RP) and Non-Homogeneous Poisson Process (NHPP). The LV were compared which resulted in GRP model Type I produced the highest LV. Finally, the model was validated by using reliability models developed based on the different duration of operation days which were 1500 and 2000 operation days, respectively. The expected cumulative numbers of failures calculated by both models were then compared with the actual cumulative number of failures obtained from the model developed using 3000 operation days. Based on the comparison, both models produced similar values with the actual failure data. Hence, the developed reliability model could be used to predict the next failure of the system. It is hoped that this project and report could be used as a reference for further research and study

Customer-oriented risk assessment in Network Utilities

For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms, based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end-customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology

A New Stochastic Model for Systems Under General Repairs

Numerous stochastic models for repairable systems have been developed by assuming different time trends, and re- pair effects. In this paper, a new general repair model based on the repair history is presented. Unlike the existing models, the closed- form solutions of the reliability metrics can be derived analytically by solving a set of differential equations. Consequently, the con- fidence bounds of these metrics can be easily estimated. The pro- posed model, as well as the estimation approach, overcomes the drawbacks of the existing models. The practical use of the proposed model is demonstrated by a much-discussed set of data. Compared to the existing models, the new model is convenient, and provides accurate estimation results

Assessing the time intervals between economic recessions

Economic recessions occur with varying duration and intensity and may entail substantial losses in terms of GDP, employment, household income, and investment spending. In this work, we propose a statistical model for the time intervals between recessions that accounts for the state of the economy and the impact of market adjustments and regulatory changes. The model uses a generalized renewal process based on the Gumbel distribution (GuGRP) in which times between consecutive events are conditionally independent. We also present a novel goodness of fit test tailored to the GuGRP that validates the use of the statistical model for the analysis of recessions. Analyzing recessions in the U.S. and Europe, we demonstrate that the statistical model characterizes well recession inter-arrival times and that the model performs better than simpler, commonly used distributions. In addition, the presented statistical model enables us to compare the adjustment processes in different economies and to forecast the occurrence of future recessions

Predicting The Reliability Of A Repairable System With Competing Failure Modes

The main objective of this project is to analyze the failure data of centrifugal pumps that were provided by a petrochemical plant and to develop a reliability model to predict the failure occurrences for each failure modes

Reliability Evaluation of Common-Cause Failures and Other Interdependencies in Large Reconfigurable Networks

This work covers the impact of Interdependencies and CCFs in large repairable networks with possibility of "re-configuration" after a fault and the consequent disconnection of the faulted equipment. Typical networks with these characteristics are the Utilities, e.g. Power Transmission and Distribution Systems, Telecommunication Systems, Gas and Water Utilities, Wi Fi networks. The main issues of the research are: (a) Identification of the specific interdependencies and CCFs in large repairable networks, and (b)Evaluation of their impact on the reliability parameters (load nodes availability, etc.). The research has identified (1) the system and equipment failure modes that are relevant to interdependencies and CCF, and their subsequent effects, and (2) The hidden interdependencies and CCFs relevant to control, supervision and protection systems, and to the automatic change-over systems, that have no impact in normal operation, but that can cause relevant out-of-service when the above automatic systems are called to operate under and after fault conditions. Additionally methods were introduced to include interdependencies and CCFs in the reliability and availability models. The results of the research include a new generalized approach to model the repairable networks for reliability analysis, including Interdependencies/CCFs as a main contributor. The method covers Generalized models for Nodes, Branches and Load nodes; Interdependencies and CCFs on Networks / Components; System Interdependencies/CCFs; Functional Interdependencies/CCFs; Simultaneous and non-simultaneous Interdependencies/CCFs. As an example detailed Interdependency/CCFs analysis and generalized model of an important network structure (a "RING" with load nodes) has been analyzed in detail

Modeling repairable system failure data using NHPP realiability growth mode.

Stochastic point processes have been widely used to describe the behaviour of repairable systems. The Crow nonhomogeneous Poisson process (NHPP) often known as the Power Law model is regarded as one of the best models for repairable systems. The goodness-of-fit test rejects the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival times of successive failures, and so cannot be applied to nonhomogeneous Poisson process