Superimposed Renewal Processes in Reliability

This paper reviews the existing literature on the superimposed renewal process, with its foci on probabilistic and statistical properties, statistical inference, and applications in reliability analysis and maintenance policy optimisation. It then proposes future research topics

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

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Component Reliability Estimation From Partially Masked and Censored System Life Data Under Competing Risks.

This research presents new approaches to the estimation of component reliability distribution parameters from partially masked and/or censored system life data. Such data are common in continuous production environments. The methods were tested on Monte Carlo simulated data and compared to the only alternative suggested in literature. This alternative did not converge on many masked datasets. The new methods produce accurate parameter estimates, particularly at low masking levels. They show little bias. One method ignores masked data and treats them as censored observations. It works well if at least 2 known-cause failures of each component type have been observed and is particularly useful for analysis of any size datasets with a small fraction of masked observations. It provides quick and accurate estimates. A second method performs well when the number of masked observations is small but forms a significant portion of the dataset and/or when the assumption of independent masking does not hold. The third method provides accurate estimates when the dataset is small but contains a large fraction of masked observations and when independent masking is assumed. The latter two methods provide an indication which component most likely caused each masked system failure, albeit at the price of much computation time. The methods were implemented in user-friendly software that can be used to apply the method on simulated or real-life data. An application of the methods to real-life industrial data is presented. This research shows that masked system life data can be used effectively to estimate component life distribution parameters in a situation where such data form a large portion of the dataset and few known failures exist. It also demonstrates that a small fraction of masked data in a dataset can safely be treated as censored observations without much effect on the accuracy of the resulting estimates. These results are important as masked system life data are becoming more prevalent in industrial production environments. The research results are gauged to be useful in continuous manufacturing environments, e.g. in the petrochemical industry. They will also likely interest the electronics and automotive industry where masked observations are common

Two methods to approximate the superposition of imperfect failure processes

Suppose a series system is composed of a number of repairable components. If a component fails, it is repaired immediately and the effectiveness of the repair may be imperfect. Then the failure process of the component can be modelled by an imperfect failure process and the failure process of the system is the superposition of the failure processes of all components. In the literature, there is a bulk of research on the superimposed renewal process (SRP) for the case where the repair on each component is assumed perfect. For the case that the component causing the system to fail is unknown and that repair on a failed component is imperfect, however, there is little research on modelling the failure process of the system. Typically, the likelihood functions for the superposition of imperfect failure processes cannot be given explicitly. Approximation-based models have to be sought. This paper proposes two methods to model the failure process of a series system in which the failure process of each component is assumed an arithmetic reduction of intensity and an arithmetic reduction of age model, respectively. The likelihood method of parameter estimation is given. Numerical examples and real-world data are used to illustrate the applicability of the proposed models

A failure process model with the exponential smoothing of intensity functions

This paper proposes a new model and investigates its special case model, both of which model the failure process of a series system composed of multiple components. We make the following assumption: (1) once the system fails, the failed component can be immediately identified and replaced with a new identical one, and (2) once the system fails, only the time of the failure is recorded; but the component that causes the system to fail is not known. The paper derives a parameter estimation method and compares the performance of the proposed models with nine other models on artificially generated data and fifteen real-world datasets. The results show that the two new models outperform the nine models in terms of the three most commonly used penalised model selection criteria, the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc) and Bayesian information criterion (BIC), respectively

Time-dependent reliability analysis of flood defences

The aim of this thesis is to investigate how the time-dependent behaviour of flood defence properties can be appropriately characterised and incorporated in a reliabilitybased approach. Such an approach is required in a maintenance optimisation framework for flood defence management. The first objective shows that existing structural reliability methods are suitable for the analysis and incorporation of asset time-dependent processes in flood defence (system) reliability. Recent progress on quantitative maintenance optimisation frameworks for flood defence management is drawn together and complemented by theory from other engineering disciplines. The second objective develops three importance measure types to indicate the relevance of the time-dependent processes in the context of a rational maintenance optimisation approach. These importance measures support practical operational management as well as maintenance optimisation model design. The third objective develops a modelling methodology to describe asset time-dependent processes of flood defences by a statistical model. The first phase in the modelling methodology is problem formulation. The second conceptualisation phase is a five-step analysis of the asset time-dependent process. Firstly, existing field observations and scientific understanding are assembled. Secondly, the excitation, ancillary and affected features and uncertainty types of the asset time-dependent process are analysed. The third step describes the character of the process conditional on the excitation. The fourth step analyses the dependencies between different asset time-dependent processes. The fifth step formulates alternative statistical models for the asset time-dependent process. The last phase in the modelling methodology is parameter estimation, calibration and model corroboration. Historical observations on asset time-dependent processes are scarce and can either be used for further extension of this phase or Bayesian posterior updating. The fourth objective demonstrates the methods developed in this thesis in a (system) reliability model of the Dartford Creek to Swanscombe Marshes flood defence system along the Thames Estuary.EThOS - Electronic Theses Online ServiceThames Estuary 2100 Project TeamGBUnited Kingdo