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.
•Methods to approximate superimposed imperfect repair processes (SIRP) are developed.•The paper finds a SIRP does not tend towards (statistical) equilibrium.•The paper extends the moving average of intensity model.•A method to approximate the SIRP with truncated masked failure is proposed.
Warranty data contain useful information about product quality and reliability, but they are usually coarse data because they may be aggregated, delayed, censored, missing or vague. They might, ...however, be the only forms of warranty data a manufacturer has, analysing such data are therefore needed and can also be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy, and forecasting future warranty claims needed for preparing warranty reserves plans.
In last two decades, considerable research has been conducted in analysing coarse warranty data (CWD) from several different perspectives. This paper categorises different types of CWD and reviews techniques to analyse such data. It concludes with research needs in CWD.
► To review existing research on warranty data collection. ► To classify warranty data into different categories. ► To find knowledge gaps. ► To propose future research.
People undergoing mass home- and community-based quarantine are vulnerable to mental health disorders during outbreaks of coronavirus disease (COVID-19), but few studies have evaluated the associated ...psychosocial factors.
This study aimed to estimate the prevalence of anxiety and depressive symptoms and identify associated demographic and psychosocial factors in the general Chinese population during the COVID-19 pandemic quarantine period.
Participants aged 18 years or above were recruited in a cross-sectional online survey using snowball sampling from February 26-29, 2020. The survey included questions on demographics, family relationships, chronic diseases, quarantine conditions, lifestyle, COVID-19 infection, and anxiety and depressive symptoms. Logistic regression analyses were conducted to identify factors associated with elevated anxiety or depressive symptoms.
Out of 2331 participants, 762 (32.7%) experienced elevated anxiety or depressive symptoms. Nine risk factors associated with anxiety or depressive symptoms included younger age, reduced income, having cancer or other chronic diseases, having family members living with cancer, concerns related to COVID-19 infection for themselves or family members, living alone, having family conflicts, having <3 or >8 hours of sedentary time per day, and worsened sleep quality.
The findings highlight an urgent need for psychological support for populations at high risk for elevated anxiety or depressive symptoms during the COVID-19 pandemic.
Preventive maintenance (PM) is a maintenance program with activities initiated at predetermined intervals, or according to prescribed criteria, and intended to reduce the probability of failure, or ...the degradation of the functioning of an item. In the literature, a number of PM models have been introduced to depict the effectiveness of PM. Based on these models, approaches to scheduling PM policies have been considerably studied. This paper attempts to review existing PM models, and investigate their inter-relationships. We then categorize these models into three classes: linear, nonlinear, and a hybrid of both. These three PM model classes depict the relationships of the hazard functions before, and after a PM. Possible extensions to these three PM models are discussed. The statistical properties for models are derived, and approaches to optimizing the PM policy are given.
Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear ...Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation.
To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.
•Models for the failure process of a repairable multicomponent system are proposed.•The performance of the proposed models is compared with that of nine other models.•Artificially generated data and ...real-world datasets are used for model validation.•Three model selection criteria are used in model comparison.•The proposed models outperform the nine existing models based on the criteria.
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.
Modelling the failure process of a repairable system is vitally important in many industrial sectors such as the offshore industry and the transport industry, in which properly maintaining assets is ...needed. Among the various models, the geometric process (GP) has been widely applied, mainly in the reliability and maintenance engineering, since its introduction. A book on the GP was published in 2007 and included the GP‐related research by that year. However, since then, much research has been conducted, which creates a necessity to review the existing publications relating to the GP, its various extensions and applications since 2007. This paper serves for this purpose.
Warranty claims are not always due to product failures. They can also be caused by two types of human factors. On the one hand, consumers might claim warranty due to misuse and/or failures caused by ...various human factors. Such claims might account for more than 10% of all reported claims. On the other hand, consumers might not be bothered to claim warranty for failed items that are still under warranty, or they may claim warranty after they have experienced several intermittent failures. These two types of human factors can affect warranty claim costs. However, research in this area has received rather little attention.
In this paper, we propose three models to estimate the expected warranty cost when the two types of human factors are included. We consider two types of failures: intermittent and fatal failures, which might result in different claim patterns. Consumers might report claims after a fatal failure has occurred, and upon intermittent failures they might report claims after a number of failures have occurred. Numerical examples are given to validate the results derived.
•Introduced a method of constructing asymmetric copulas.•Discovered the existence of tail dependence of two-dimensional reliability data.•Applied the proposed method to warranty data analysis.
...Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes a new method of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world.
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