Shahrzad Oveisi, Ali Moeini, Sayeh Mirzaei, Mohammad Ali Farsi
Reliability growth models are commonly categorized into two primary groups: parametric and non‐parametric models. Parametric models, known as Software Reliability Growth Models (SRGM) rely on a set of hypotheses that can potentially affect the accuracy of model predictions, while non‐parametric models (such as neural networks) can predict the model solely based on training data without any assumptions regarding the model itself. In this paper, we propose several methods to enhance prediction accuracy in software reliability context. More specifically, we, on one hand, introduce two gradient‐based techniques for estimating parameters of classical SRGMs. On the other, we propose methods involving LSTM Encoder–Decoder and Bayesian approximation within Langevin Gradient and Variational inference neural networks. To evaluate our proposed models' performance, we compare them with various neural network‐based software reliability models using three real‐world software failure datasets and utilizing the Mean Square Error (MSE) as a model comparison criterion. The experimental results indicate that our proposed non‐parametric models outperform most classical parametric and non‐parametric models.
{"title":"Software reliability prediction: A machine learning and approximation Bayesian inference approach","authors":"Shahrzad Oveisi, Ali Moeini, Sayeh Mirzaei, Mohammad Ali Farsi","doi":"10.1002/qre.3616","DOIUrl":"https://doi.org/10.1002/qre.3616","url":null,"abstract":"Reliability growth models are commonly categorized into two primary groups: parametric and non‐parametric models. Parametric models, known as Software Reliability Growth Models (SRGM) rely on a set of hypotheses that can potentially affect the accuracy of model predictions, while non‐parametric models (such as neural networks) can predict the model solely based on training data without any assumptions regarding the model itself. In this paper, we propose several methods to enhance prediction accuracy in software reliability context. More specifically, we, on one hand, introduce two gradient‐based techniques for estimating parameters of classical SRGMs. On the other, we propose methods involving LSTM Encoder–Decoder and Bayesian approximation within Langevin Gradient and Variational inference neural networks. To evaluate our proposed models' performance, we compare them with various neural network‐based software reliability models using three real‐world software failure datasets and utilizing the Mean Square Error (MSE) as a model comparison criterion. The experimental results indicate that our proposed non‐parametric models outperform most classical parametric and non‐parametric models.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"7 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Designs for computer experiments in quantitative factors use columns with many levels. Filling the experimental space is their most important property, and there are many criteria that assess aspects of space‐filling. Recently, Tian and Xu proposed a stratification pattern for assessing the stratification‐related space‐filling properties of designs for quantitative experimental variables whose number of levels is a power of a – usually small – integer. Such designs have been named GSOAs, in generalization of the earlier proposal of strong – or stratum – orthogonal arrays (SOAs). Latin hypercube designs (LHDs) with a suitable number of levels are special cases of GSOAs. Tian and Xu proposed to use the stratification pattern as a means to ranking (G)SOAs. They reported a small simulation study in which arrays that fared well in that ranking performed well in predicting an unknown function. Shi and Xu refined the criterion and also demonstrated success of a design that fares well on their refined criterion. This paper explains the ideas behind the stratification pattern and the related ranking criteria. A practical example and several toy examples aid the illustration. The stratification pattern can be calculated using the R package SOAs, which does not only provide the pattern itself but also provides more detail in a dimension by weight table, in the spirit of the refinement by Shi and Xu.
定量因素的计算机实验设计使用具有多个层次的列。填充实验空间是其最重要的特性，有许多标准可以评估空间填充的各个方面。最近，田（Tian）和徐（Xu）提出了一种分层模式，用于评估与分层相关的定量实验变量设计的空间填充特性。此类设计被命名为 GSOA，是对早先提出的强正交阵列（SOA）或分层正交阵列（SOA）的概括。具有适当层数的拉丁超立方设计（LHD）是 GSOA 的特例。Tian 和 Xu 提议使用分层模式对 (G)SOA 进行排序。他们报告了一项小型模拟研究，结果表明在该排序中表现出色的阵列在预测未知函数时表现出色。Shi 和 Xu 改进了这一标准，并展示了在他们改进的标准中表现良好的设计。本文解释了分层模式和相关排序标准背后的理念。一个实际例子和几个玩具例子有助于说明问题。分层模式可以使用 R 软件包 SOAs 计算，该软件包不仅提供了模式本身，还根据 Shi 和 Xu 所做改进的精神，在按权重划分的维度表中提供了更多细节。
{"title":"A new kid on the block: The stratification pattern for space‐filling, with dimension by weight tables","authors":"Ulrike Grömping","doi":"10.1002/qre.3627","DOIUrl":"https://doi.org/10.1002/qre.3627","url":null,"abstract":"Designs for computer experiments in quantitative factors use columns with many levels. Filling the experimental space is their most important property, and there are many criteria that assess aspects of space‐filling. Recently, Tian and Xu proposed a stratification pattern for assessing the stratification‐related space‐filling properties of designs for quantitative experimental variables whose number of levels is a power of a – usually small – integer. Such designs have been named GSOAs, in generalization of the earlier proposal of strong – or stratum – orthogonal arrays (SOAs). Latin hypercube designs (LHDs) with a suitable number of levels are special cases of GSOAs. Tian and Xu proposed to use the stratification pattern as a means to ranking (G)SOAs. They reported a small simulation study in which arrays that fared well in that ranking performed well in predicting an unknown function. Shi and Xu refined the criterion and also demonstrated success of a design that fares well on their refined criterion. This paper explains the ideas behind the stratification pattern and the related ranking criteria. A practical example and several toy examples aid the illustration. The stratification pattern can be calculated using the R package SOAs, which does not only provide the pattern itself but also provides more detail in a dimension by weight table, in the spirit of the refinement by Shi and Xu.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"55 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammed Kadhim Shanshool, Shashibhushan B. Mahadik, Dadasaheb G. Godase, Michael B. C. Khoo
The Shewhart control chart is a prominent tool for identifying the changes in process parameters that are of large magnitude, however, it has reduced ability to identify the process changes of small magnitudes. On the other hand, an exponentially weighted moving average (EWMA) control chart is superior to the Shewhart chart in identifying process changes of small magnitudes but it is less proficient than the later chart in identifying changes of large magnitudes. This paper suggests nonparametric combined Shewhart‐EWMA (CSE) charts based on the sign statistic for the process location and process dispersion. The statistical performance measures of these charts are obtained using a Markov chain approach. The numerical comparisons revealed that the performance of a CSE chart lies within the range of the Shewhart sign and EWMA sign charts for identifying a process change of any magnitude. A real‐data example is provided to illustrate the mechanism of the chart.
{"title":"The combined Shewhart–EWMA sign charts","authors":"Mohammed Kadhim Shanshool, Shashibhushan B. Mahadik, Dadasaheb G. Godase, Michael B. C. Khoo","doi":"10.1002/qre.3625","DOIUrl":"https://doi.org/10.1002/qre.3625","url":null,"abstract":"The Shewhart control chart is a prominent tool for identifying the changes in process parameters that are of large magnitude, however, it has reduced ability to identify the process changes of small magnitudes. On the other hand, an <jats:italic>exponentially weighted moving average</jats:italic> (EWMA) control chart is superior to the Shewhart chart in identifying process changes of small magnitudes but it is less proficient than the later chart in identifying changes of large magnitudes. This paper suggests nonparametric <jats:italic>combined Shewhart‐EWMA</jats:italic> (CSE) charts based on the sign statistic for the process location and process dispersion. The statistical performance measures of these charts are obtained using a Markov chain approach. The numerical comparisons revealed that the performance of a CSE chart lies within the range of the Shewhart sign and EWMA sign charts for identifying a process change of any magnitude. A real‐data example is provided to illustrate the mechanism of the chart.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"61 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.
{"title":"A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold","authors":"Li'Na Ren, Kangning Li, Xueliang Li, Ziqian Wang","doi":"10.1002/qre.3623","DOIUrl":"https://doi.org/10.1002/qre.3623","url":null,"abstract":"In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"92 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a new way of modelling imperfect maintenance in degradation models, by assuming that maintenance affects only a part of the degradation process. More precisely, the global degradation process is the sum of two dependent Wiener processes with drift. Maintenance has an effect of the ‐type on only one of these processes: it reduces the degradation level of a quantity which is proportional to the amount of degradation of this process accumulated since previous maintenance. Two particular cases of the model are considered: perturbed and partial replacement models. The usual model is also a specific case of this new model. The system is regularly inspected in order to measure the global degradation level. Two observation schemes are considered. In the complete scheme, the degradation levels are measured both between maintenance actions and at maintenance times (just before and just after). In the general scheme, the degradation levels are measured only between maintenance actions. The maximum likelihood estimation of the model parameters is studied for both observation schemes in both particular models. The quality of the estimators is assessed through a simulation study.
{"title":"Modelling and inference for a degradation process with partial maintenance effects","authors":"Margaux Leroy, Christophe Bérenguer, Laurent Doyen, Olivier Gaudoin","doi":"10.1002/qre.3618","DOIUrl":"https://doi.org/10.1002/qre.3618","url":null,"abstract":"This paper proposes a new way of modelling imperfect maintenance in degradation models, by assuming that maintenance affects only a part of the degradation process. More precisely, the global degradation process is the sum of two dependent Wiener processes with drift. Maintenance has an effect of the ‐type on only one of these processes: it reduces the degradation level of a quantity which is proportional to the amount of degradation of this process accumulated since previous maintenance. Two particular cases of the model are considered: perturbed and partial replacement models. The usual model is also a specific case of this new model. The system is regularly inspected in order to measure the global degradation level. Two observation schemes are considered. In the complete scheme, the degradation levels are measured both between maintenance actions and at maintenance times (just before and just after). In the general scheme, the degradation levels are measured only between maintenance actions. The maximum likelihood estimation of the model parameters is studied for both observation schemes in both particular models. The quality of the estimators is assessed through a simulation study.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"58 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial for the special issue on experimental design for reliability and life testing","authors":"Rong Pan","doi":"10.1002/qre.3620","DOIUrl":"https://doi.org/10.1002/qre.3620","url":null,"abstract":"","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"2 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a multivariate process monitoring scheme based on the rank‐energy statistics is proposed which is suitable for high‐dimensional applications such as sensorless drive diagnosis. The rank‐energy statistic is based on multivariate ranks that is grounded on the measure transportation theory. Univariate ranks could be interpreted as a solution to an optimisation problem involving a given set of observations of size and the set {}. Recently, attaining greater robustness than spatial sign or depth‐based ranks, multivariate ranks are proposed as solutions to such optimisation problem in multivariate settings (measure transportation problem). The proposed multivariate process monitoring scheme based on the rank‐energy statistic, subsequently, attains greater robustness than existing nonparametric multivariate process monitoring methods based on spatial sign or depth‐based ranks. The proposed method is also applicable to high‐dimensional data unlike some of the existing nonparametric multivariate process monitoring methods. A rigorous simulation study demonstrates its effective shift detection ability and other important features. A practical application of the proposed method is demonstrated with the sensorless drive diagnosis case study.
{"title":"Distribution‐free multivariate process monitoring: A rank‐energy statistic‐based approach","authors":"Niladri Chakraborty, Maxim Finkelstein","doi":"10.1002/qre.3619","DOIUrl":"https://doi.org/10.1002/qre.3619","url":null,"abstract":"In this paper, a multivariate process monitoring scheme based on the rank‐energy statistics is proposed which is suitable for high‐dimensional applications such as sensorless drive diagnosis. The rank‐energy statistic is based on multivariate ranks that is grounded on the measure transportation theory. Univariate ranks could be interpreted as a solution to an optimisation problem involving a given set of observations of size and the set {}. Recently, attaining greater robustness than spatial sign or depth‐based ranks, multivariate ranks are proposed as solutions to such optimisation problem in multivariate settings (measure transportation problem). The proposed multivariate process monitoring scheme based on the rank‐energy statistic, subsequently, attains greater robustness than existing nonparametric multivariate process monitoring methods based on spatial sign or depth‐based ranks. The proposed method is also applicable to high‐dimensional data unlike some of the existing nonparametric multivariate process monitoring methods. A rigorous simulation study demonstrates its effective shift detection ability and other important features. A practical application of the proposed method is demonstrated with the sensorless drive diagnosis case study.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"14 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is well known that process control index estimators are inflated when naively applied to positively autocorrelated data. The autocorrelation is a nuisance and not a feature that is captured in the process capability indices. This paper proposes exhaustive systematic sampling to create a pooled variance estimator that replaces the biased estimator of the process data standard deviation when data are autocorrelated. The proposed method is effective because the observations within a systematic sample are spread out in time and should be less correlated with each other as a result. It is similar to Bayesian thinning as a strategy for reducing the impact of autocorrelation except no observations are dropped. Properties of estimated process control indices are derived using quadratic forms and large sample theory that is nonparametric in the sense no distribution or time series model is assumed. Approximately unbiased estimates can be achieved for sufficiently large systematic sampling interval. The proposed method is compared to the time series method in a simulation study that demonstrates similar performance. The proposed method is applied to two examples that use because the target is not the midpoint of the specification limits and the mean differs from the target.
{"title":"Unbiased process capability estimation for autocorrelated data using exhaustive systematic sampling","authors":"Scott D. Grimshaw, Zhupeng Guo, Tyler Duke","doi":"10.1002/qre.3617","DOIUrl":"https://doi.org/10.1002/qre.3617","url":null,"abstract":"It is well known that process control index estimators are inflated when naively applied to positively autocorrelated data. The autocorrelation is a nuisance and not a feature that is captured in the process capability indices. This paper proposes exhaustive systematic sampling to create a pooled variance estimator that replaces the biased estimator of the process data standard deviation when data are autocorrelated. The proposed method is effective because the observations within a systematic sample are spread out in time and should be less correlated with each other as a result. It is similar to Bayesian thinning as a strategy for reducing the impact of autocorrelation except no observations are dropped. Properties of estimated process control indices are derived using quadratic forms and large sample theory that is nonparametric in the sense no distribution or time series model is assumed. Approximately unbiased estimates can be achieved for sufficiently large systematic sampling interval. The proposed method is compared to the time series method in a simulation study that demonstrates similar performance. The proposed method is applied to two examples that use because the target is not the midpoint of the specification limits and the mean differs from the target.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"60 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy.
{"title":"Degradation index‐based prediction for remaining useful life using multivariate sensor data","authors":"Wenda Kang, Geurt Jongbloed, Yubin Tian, Piao Chen","doi":"10.1002/qre.3615","DOIUrl":"https://doi.org/10.1002/qre.3615","url":null,"abstract":"The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation.
{"title":"A modular model for reliability analysis model of PMS with multiple K/N subsystems and mixed shocks","authors":"Weijie Wang, Xiangyu Li, Xiaoyan Xiong","doi":"10.1002/qre.3614","DOIUrl":"https://doi.org/10.1002/qre.3614","url":null,"abstract":"In this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"9 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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