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Neural Computing And Applications (09410643)37(17)pp. 10621-10645
The goal of this research is to present an approach that predicts the remaining useful life (RUL) estimation of a turbofan engine system within specific time frames of aircraft operation. In the first phase, due to the high number of features, a group decision-making approach for feature selection is proposed and performed based on various supervised and unsupervised methods. In the second phase, among various machine learning and deep learning approaches, bidirectional long short-term memory (BiLSTM) and multilayer perceptron (MLP) that provided more appropriate results are selected. Unlike most previous studies that focused on determining the status of the equipment (whether it is healthy or faulty), the main objective of this research is to predict RUL. Finally, in the third phase, thorough analysis using explainable AI (XAI) was conducted concerning the importance of features and an investigation into features that led to an increase in errors in some intervals. The results show that the presented approach has been able to predict the RUL well, although it is biased in some time intervals for all turbofans, and the features related to this bias prediction are determined by using XAI. They can be further investigated by the maintenance department. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Communications in Statistics - Theory and Methods (1532415X)52(21)pp. 7564-7575
Mixture of survival and hazard functions have been widely applied to the analysis of data from heterogeneous populations. From the Bayesian point of view, two different predictive mixture hazard rates can be considered: prior and posterior predictive hazard rates. In this paper, we consider a heterogeneous population consists of two sub-populations with different hazard rates where each one follows a Cox proportional hazards model. A prior predictive mixture hazard model is proposed to estimate the hazard rate of the population through the assessment of some potential regression covariates. Under right-censoring, the estimating equations based on martingale are developed to estimate the parameters of the assumed mixture model. The large sample properties of the proposed estimators are established. The finite sample behavior of the resulting estimators is evaluated through simulation studies, and the approach is also applied to a kidney cancer data set collected from a clinical trial. © 2022 Taylor & Francis Group, LLC.
Statistics (02331888)56(1)pp. 147-163
This paper introduces a dynamic divergence measure to assess the discrepancy between the distribution functions of two inactivity lifetime random variables. Various time-dependent results on the proposed divergence measure in connection to other well-known measures in reliability engineering and survival studies are investigated. Some aging and monotonicity properties of such a measure are also studied. Furthermore, the proposed criterion is examined in two general classes of transformation models which results in some well-known models in the lifetime studies and survival analysis. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Statistics (02331888)56(5)pp. 1113-1132
The quantile function can be considered an efficient alternative to the distribution function in cases where the quantile functions are tractable while the distribution functions do not have explicit forms. In this paper, we introduce a quantile-based definition of the ϕ-divergence family of measures to assess the discrepancy between two random variables. The proposed measure defines a rich family of divergence measures and includes various common divergences as special cases. Some properties of these measures of divergence are studied and several examples are also provided. Dynamic versions of the quantile-based ϕ-divergence family of measures between residual and past lifetimes are developed and their properties are studied. The suggested divergence measures are also examined in a general class of transformed models which results in some well-known models in the lifetime studies and survival analysis. Furthermore, the non-parametric estimations of the proposed divergence measures are discussed, and the performance of the resulting estimators is evaluated via simulation. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Statistics (02331888)54(6)pp. 1311-1328
A time-dependent divergence measure is proposed to compare the survival functions of two lifetime random variables. It is shown that the proposed measure ranges between (Formula presented.) and for the proportional hazards case has the metric properties. Several properties of the divergence measure are investigated, among others, it is shown that the divergence between two survival functions does not depend on time if and only if they follow the proportional hazards model. The measure is also examined for various other well-known survival models such as the proportional odds model. The estimation of the suggested divergence measure for survival data is also discussed, and the asymptotic normal distribution of the resulting estimator is established. The proposed estimation is evaluated via simulation and further employed to compare the effects of two treatment groups on the overall survival times of kidney cancer patients. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Mathematical Methods of Statistics (19348045)29(3)pp. 135-148
Abstract: Recently, a time-dependent measure of divergence has been introduced by Mansourvar and Asadi (2020) to assess the discrepancy between the survival functions of two residual lifetime random variables. In this paper, we derive various time-dependent results on the proposed divergence measure in connection to other well-known measures in reliability engineering. The proposed criterion is also examined in mixture models and a general class of survival transformation models which results in some well-known models in the lifetime studies and survival analysis. In addition, the time-dependent measure is employed to evaluate the divergence between the lifetime distributions of k-out-of-n systems and also to assess the discrepancy between the distribution functions of the epoch times of a non-homogeneous Poisson process. © 2020, Allerton Press, Inc.
International Journal of Biostatistics (15574679)15(1)
The mean past lifetime provides the expected time elapsed since the failure of a subject given that he/she has failed before the time of observation. In this paper, we propose the proportional mean past lifetime model to study the association between the mean past lifetime function and potential regression covariates. In the presence of left censoring, martingale estimating equations are developed to estimate the model parameters, and the asymptotic properties of the resulting estimators are studied. To assess the adequacy of the model, a goodness of fit test is also investigated. The proposed method is evaluated via simulation studies and further applied to a data set. © 2019 Walter de Gruyter GmbH, Berlin/Boston 2019.
Precision Engineering (01416359)52pp. 201-210
Tolerance analysis provides valuable information regarding performance of manufacturing process. It allows determining the maximum possible variation of a quality feature in production. Previous researches have focused on application of tolerance analysis to the design of mechanical assemblies. In this paper, a new statistical analysis was applied to manufactured products to assess achieved tolerances when the process is known while using capability ratio and expanded uncertainty. The analysis has benefits for process planning, determining actual precision limits, process optimization, troubleshoot malfunctioning existing part. The capability measure is based on a number of measurements performed on part's quality variable. Since the ratio relies on measurements, elimination of any possible error has notable negative impact on results. Therefore, measurement uncertainty was used in combination with process capability ratio to determine conformity and nonconformity to requirements for quality characteristic of a population of workpieces. A case study of sheared billets was described where proposed technique was implemented. The use of ratio was addressed to draw conclusions about non-conforming billet's weight expressed in parts per million (ppm) associated with measurement uncertainty and tolerance limits. The results showed significant reduction of conformance zone due to the measurement uncertainty. © 2017 Elsevier Inc.
Lifetime Data Analysis (15729249)23(3)pp. 426-438
Although mean residual lifetime is often of interest in biomedical studies, restricted mean residual lifetime must be considered in order to accommodate censoring. Differences in the restricted mean residual lifetime can be used as an appropriate quantity for comparing different treatment groups with respect to their survival times. In observational studies where the factor of interest is not randomized, covariate adjustment is needed to take into account imbalances in confounding factors. In this article, we develop an estimator for the average causal treatment difference using the restricted mean residual lifetime as target parameter. We account for confounding factors using the Aalen additive hazards model. Large sample property of the proposed estimator is established and simulation studies are conducted in order to assess small sample performance of the resulting estimator. The method is also applied to an observational data set of patients after an acute myocardial infarction event. © 2016, Springer Science+Business Media New York.
Scandinavian Journal of Statistics (14679469)43(2)pp. 487-504
The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive-multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time-varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial. © 2016 Board of the Foundation of the Scandinavian Journal of Statistics.
Journal of Applied Statistics (02664763)42(12)pp. 2597-2613
A mean residual life function (MRLF) is the remaining life expectancy of a subject who has survived to a certain time point. In the presence of covariates, regression models are needed to study the association between the MRLFs and covariates. If the survival time tends to be too long or the tail is not observed, the restricted mean residual life must be considered. In this paper, we propose the proportional restricted mean residual life model for fitting survival data under right censoring. For inference on the model parameters, martingale estimating equations are developed, and the asymptotic properties of the proposed estimators are established. In addition, a class of goodness-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and the approach is applied to a set of real life data collected from a randomized clinical trial. © 2015 Taylor & Francis.
De lichtenberg, T.H.,
Hermann, G.G.,
Roørth, M.,
Larsen, M.H.,
Mansourvar, Z.,
Holm, M.L.,
Scheike, T.H. Scandinavian Journal of Urology (21681813)48(4)pp. 379-386
Objective. The aim of this study was to evaluate overall survival (OS) after treatment of metastatic renal cell carcinoma (mRCC) following the introduction of tyrosine kinase inhibitors (TKIs) and mammalian target of rapamycin (mTOR) inhibitors. Material and methods. One-hundred and forty-three consecutive mRCC patients were given immunotherapy (n = 59), TKIs (n = 49) or sequential therapy (IMM→TKI group; n = 35). The TKI group included patients with higher age (p < 0.001), worse performance status (p = 0.005) and higher risk profile (p < 0.001) than the other two treatment groups. Number of metastases and sites and tumour histology did not differ between groups. Results. First line immunotherapy gave a median OS of 16.3 months and first line TKIs 10.9 months (p = 0.003). Survival longer than 5 years was limited to immunotherapy. Sarcomatoid component, metastatic sites, papillary histology, stage, performance status and white cell blood count were related to poor OS. Using multivariate analyses to adjust for risk predictors the difference in OS disappeared. Median OS before and after introduction of TKIs was 16 months and 14 months, respectively (p = 0.189). Memorial Sloan Kettering Cancer Center (MSKCC) risk groups were related to OS (p < 0.001). Heng's prognostic criteria appeared slightly more predictive than MSKCC (p = 0.12). Metastasectomy (n = 42) may improve OS [surgery: median OS 18.8 months, 95% confidence interval (CI) 12.3-48.5; no surgery: median OS 15 months, 95% CI 10.4-16.5; p = 0.07]. Conclusions. MSKCC and Heng's prognostic algorithms were valid for prognostication and can be used for individual planning of treatment and follow-up. Surgical removal of metastases may improve OS. © 2014 Informa Healthcare.
