Stefanka Chukova

Hazard rate functions of natural and manufactured systems often show a bathtub shaped failure rate. A high early rate of failures is followed by an extended period of useful working life where failures are rare, and finally the failure rate increases as the system reaches the end of its life. Parametric modelling of such hazard rate functions can lead to unnecessarily restrictive assumptions on the function shape, however the most common non-parametric estimator (the Kaplan-Meier estimator) does not allow specification of the requirement that it be bathtub shaped. In this paper we extend the Lo and Weng (1989) approach and specify four nonparametric bathtub hazard rate functions drawn from Gamma Process Priors. We implement and demonstrate simulation for these four models.

In this article, we present a model for delayed reporting of faults: multiple nonfatal faults are accumulated and then simultaneously reported and repaired. The reporting process is modeled as a stochastic process dependent on the underlying stochastic process generating the faults. We derive the joint distribution of the reporting times and numbers of reported faults, giving general results and results specific to faults generated by a Poisson process. We investigate a number of extensions to the basic model, including multiple fault types (including invisible and fatal faults), preventative maintenance, and customer rush. We show how to simulate from the model and implement maximum likelihood parameter estimation in a simulated dataset and a real dataset of warranty claims from a car manufacturer.

It is common in the literature on the reliability and maintenance of repairable systems to model the repair times as instantaneous. However, this is an unreasonable assumption for some complex systems, especially those requiring a high level of reliability, and such systems may spend a significant proportion of their lifetimes under maintenance and repair. We model the ageing of such a system with alternating stochastic processes. Operational times are generated at random and may have an increasing failure rate. Repair times are generated from a random process where the repair time is related to the hazard rate at failure. This yields lengthened repair times at late stages in a system subject to an increasing failure hazard rate but also accommodates long repair times at young ages in systems with a bathtub‐shaped hazard rate function. We derive analytic results for a set of special cases of the model, show how simulation and inference can be carried out, and apply our method to real data from a large car manufacturer.

In this paper, motivated by the risk process, we introduce and study a new point process called geometric Pólya-Aeppli process (GPAP), with underlying exponential distribution. We give two equivalent definitions of the process and discuss some of its properties, such as the distribution of the number of GPAP events up to time t, the distribution of the waiting times, etc. The new process is an extension of the well-known Pólya-Aeppli process, as well as the standard geometric process with underlying exponential distribution.

The geometric process (GP) introduced by Lam (1988) can be used to model the occurrence of events that have an underlying monotonic trend. This type of trend can be observed in many practical problems in different areas of our everyday life, such as engineering, epidemiology, business, and health. To provide improved flexibility in modeling phenomena and situations involving monotonic trends, a variety of extensions of the GP have been proposed. This chapter provides an overview of geometric and geometric-like processes (GLPs). We include some basic definitions, facts, and references for these processes. We also provide a taxonomy of GLPs showing the connections between the various approaches taken by researchers in this area. Some of their applications in areas such as maintenance, warranty analysis, and others are also outlined with appropriate references. As a specific application of the GP we present the alternating geometric process (AGP) in which a system subject to failure experiences an alternating sequence of episodes of operation and repair. We provide some specific results for the AGP in the setting of items under warranty and illustrate the model with a real data application.

Reliability literature on modeling failures of repairable systems mostly deals with systems having monotonically increasing hazard/failure rates. When the hazard rate of a system is non-monotonic, models developed for monotonically increasing failure rates cannot be effectively applied without making assumptions on the types of repair performed following system failures. For instance, for systems having bathtub-shaped hazard rates, it is assumed that during the initial, decreasing hazard rate phase, all repairs are minimal. These assumptions on the type of general repair can be restrictive. In order to relax these assumptions, it has been suggested that general repairs in the initially decreasing phase can be modeled as “aging” the system. This approach however does not preserve the order of effectiveness of the types of general repair as defined in the literature. In this article, we develop a set of models to address these shortcomings. We propose a new stochastic process to model consecutive failures of repairable systems having non-monotonic, specifically bathtub-shaped, hazard rates, where the types of general repair are not restricted and the order of the effectiveness of the types of repair is preserved. The proposed models guarantee that a repaired system is at least as reliable as one that has not failed (or equivalently one that has been minimally repaired). To illustrate the models, we present multiple examples and simulate the failure-repair process and estimate the quantities of interest.

In this study, we model the warranty claims process and evaluate the warranty servicing costs under non-renewing, renewing and restricted renewing free repair warranties. We assume that the repair time for rectifying the claims is non-zero and the repair cost is a function of the length of the repair time. To accommodate the ageing of the product and repair equipment, we use a decreasing geometric process to model the consecutive operational times and an increasing geometric process to model the consecutive repair times. We identify and study the alternating geometric process, which is an alternating process with cycles consisting of the item’s operational time followed by the corresponding repair time. We derive new results for the alternating geometric process in a finite horizon and use them to evaluate the warranty costs over the warranty period and over the life cycle of the product under a non-renewing free repair warranty, a renewing free repair warranty and a restricted renewing free repair warranty. Properties of the model are demonstrated using a simulation study and by fitting the models to real data from an automotive manufacturer.