{"title":"Using Multiple Regression Models and Methods to Estimate Fatigue-Life Distributions and Construct Constant-Life Diagrams","authors":"Diaz F. Aksioma, William Q. Meeker, Huaiqing Wu","doi":"10.1002/asmb.70074","DOIUrl":"https://doi.org/10.1002/asmb.70074","url":null,"abstract":"<p>Fatigue is the most common reliability failure mechanism and has been studied widely since the 19th century. Material specimens are used in laboratory experiments to obtain fatigue test data. An <i>S-N</i> curve is used to depict the relationship between the stress (or strain) <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow>\u0000 <annotation>$$ S $$</annotation>\u0000 </semantics></math> and the number of cycles to failure <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>. Statistical methods are used to fit an <i>S-N</i> relationship that can be used further to estimate properties of fatigue-life and fatigue-strength distributions. In particular, one can obtain estimates and confidence intervals for distribution quantiles and failure probabilities. Likelihood-based and Bayesian inference methods are the foundational methods for statistical estimation and quantification of statistical uncertainty. Improvements in computing technology (hardware, software, and computational methods) have made it practicable to use these methods for important applications. This paper uses these foundational methods to model fatigue life and fatigue strength as a function of the experimental variables stress amplitude, mean stress, and stress ratio, extending and importantly improving methods currently used for such applications. We illustrate the methods with two different data sets. The first example is based on <i>S-N</i> test data of a composite material widely used to manufacture wind turbine blades, where the fatigue-life model is specified, and the fatigue-strength is induced. The second example is based on <i>S-N</i> test data of an aluminum alloy commonly used in aerospace applications. Because of the complicated features of the fatigue life data for this example, we use a specified fatigue-strength model and show how it can be used to make inferences about the corresponding fatigue-life model. Finally, we show how to use these multiple regression models to obtain constant-life diagrams (CLDs), an engineering tool that provides a visual representation of the quantiles of a fatigue-life distribution as a function of stress amplitude and mean stress. We compare CLDs based on multiple regression models with CLDs obtained by using separate simple regression models for each level of stress ratio.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Constructing a Sample-Size Adaptable Sampling System Based on a Third-Generation Process Capability Index","authors":"To-Cheng Wang","doi":"10.1002/asmb.70076","DOIUrl":"https://doi.org/10.1002/asmb.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>Acceptance sampling plans (ASP) help practitioners economically and efficiently verify product quality, serving as a widely applied statistical quality control method. Among ASPs, the simplest form is the single sampling plan (SSP). Recently, acceptance sampling systems incorporating two SSPs as decision rules for lot disposition have gained significant attention due to their superior performance. Depending on the switching mechanism for decision rules, acceptance sampling systems can be categorized into quick switching systems and two-plan sampling systems (TSS), where TSS exhibits greater flexibility and adaptability in its rule-switching mechanism. In this study, we construct a TSS with a sample size adjustment mechanism and integrate it with a third-generation process capability index. Detailed investigation and analysis reveal that the proposed method provides more substantial incentives for suppliers, as its properties penalize suppliers who submit poor-quality lots through increased sample size while rewarding suppliers who submit high-quality lots by requiring smaller sample sizes. Finally, we demonstrate the application of the proposed method through a practical case study.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The GARCH Model Driven by Fractional Brownian Motion","authors":"Yuecai Han, Weiying Wang, Dingwen Zhang","doi":"10.1002/asmb.70071","DOIUrl":"https://doi.org/10.1002/asmb.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>This article presents a novel extension of the GARCH model incorporating weighted liquidity, modeled by fractional Brownian motion. The existence of a stationary solution is proven, and the higher-order moments are calculated to illustrate the statistical properties of the model. Analysis of the auto-correlation function of the squared process confirms the long-term memory characteristic of the model. Numerical simulations are employed to validate the theoretical findings, demonstrating the significance of the model in the financial market.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal Exact Designs of Multiresponse Experiments Under Linear and Sparsity Constraints","authors":"Lenka Filová, Pál Somogyi, Radoslav Harman","doi":"10.1002/asmb.70072","DOIUrl":"https://doi.org/10.1002/asmb.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a computational approach to constructing exact designs on finite design spaces that are optimal for multiresponse regression experiments under a combination of the standard linear and specific ‘sparsity’ constraints. The linear constraints address, for example, limits on multiple resource consumption and the problem of optimal design augmentation, while the sparsity constraints control the set of distinct trial conditions utilized by the design. The key idea is to construct an artificial optimal design problem that can be solved using any existing mathematical programming technique for univariate-response optimal designs under pure linear constraints. The solution to this artificial problem can then be directly converted into an optimal design for the primary multivariate-response setting with combined linear and sparsity constraints. We demonstrate the utility and flexibility of the approach through a dose-response case study with multivariate responses that sequentially adds constraints on safety, efficacy, and cost, where cost also depends on the number of distinct doses used.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust Planning of Accelerated Destructive Degradation Tests: Model Discrimination, Parameter Estimation, and Lifetime Prediction","authors":"Lin Wu, Xiao-Dong Zhou, Rong-Xian Yue","doi":"10.1002/asmb.70070","DOIUrl":"https://doi.org/10.1002/asmb.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Accelerated destructive degradation tests (ADDTs) play a cr\u0000ucial role in providing timely reliability information for long-life products. Previous research has primarily focused on identifying optimal ADDT plans for accurately predicting specific quantiles of the failure-time distribution under use conditions, utilizing a given degradation model. However, a common challenge during the experimental design phase is the lack of confidence in selecting a model that accurately represents the underlying data. In this paper, we introduce a novel approach that specifically addresses optimal ADDT planning for model discrimination. We propose KL-optimal design criteria to enhance the model discrimination capability of ADDTs. Moreover, we present compound DKL- and CKL-optimal design criteria to ensure that ADDT plans can simultaneously achieve effective model discrimination alongside accurate parameter estimation or precise quantile prediction. We establish equivalence theorems to validate the optimality of the proposed designs. Meanwhile, we employ the particle swarm optimization algorithm to efficiently compute optimal ADDT plans. Through a practical application and sensitivity analysis, we demonstrate the effectiveness and robustness of our optimal designs. Our proposed method offers engineers with a valuable solution for developing optimal ADDT plans that meet multiple objectives.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Three-Way Data Analysis With Explainable Tucker3 Clustering (XT3Clus)","authors":"Mariaelena Bottazzi Schenone, Tiziano Iannaccio, Ilaria Mozzetta, Maurizio Vichi","doi":"10.1002/asmb.70069","DOIUrl":"https://doi.org/10.1002/asmb.70069","url":null,"abstract":"<p>In an era of increasingly complex data, three-way arrays capturing information across units, variables and occasions are ubiquitous in fields from chemometrics to finance. However, extracting meaningful and interpretable patterns from such data remain a significant challenge. To address this, we introduce the Explainable Tucker3 Clustering (XT3Clus) methodology. XT3Clus performs clustering on units while simultaneously identifying explainable components for variables and/or occasions, significantly enhancing model interpretability. This approach functions as a constrained Tucker3 model, where each dimension is forced to contribute to a single component. The framework supports fully confirmatory, exploratory or hybrid analytical strategies. The optimization of the objective function is carried out by an efficient Alternating Least Squares algorithm. Finally, we propose a novel quantitative metric to evaluate the interpretability of a solution and confirm the practical utility of XT3Clus in three real-world scenarios.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-Objective Redundancy Allocation Problem for Systems With Weighted \u0000 \u0000 \u0000 k\u0000 \u0000 $$ k $$\u0000 -Out-of-\u0000 \u0000 \u0000 n\u0000 \u0000 $$ n $$\u0000 Subsystems Formed by Different Types of Multistate Components","authors":"Darshana Yadav, Mithu Rani Kuiti, Maxim Finkelstein","doi":"10.1002/asmb.70068","DOIUrl":"https://doi.org/10.1002/asmb.70068","url":null,"abstract":"<div>\u0000 \u0000 <p>The redundancy allocation problem (RAP) is considered as one of the important problems in reliability theory. In this paper, we consider a series system with several subsystems wherein each subsystem is a weighted <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-out-of-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math> system formed by different types of multi-state components. The degradation of the performance level (i.e., the probability of changing from a given state <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>i</mi>\u0000 </mrow>\u0000 <annotation>$$ i $$</annotation>\u0000 </semantics></math> to the next state <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>i</mi>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(i-1right) $$</annotation>\u0000 </semantics></math>) of a component of the system is modeled by the Markov process. Then, we study the multi-objective RAP problem for this system, that is, we determine the optimum number of components of each type in each subsystem so that the maximum system reliability is achieved at minimum cost. Note that the given RAP problem is of NP-hard type, and consequently, we use the controlled elitism non-dominated ranked genetic algorithm (CE-NRGA) to solve this problem. At the end, we illustrate the proposed methodology through a numerical example. Moreover, we discuss a case study to validate the proposed model.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}