Quality and Reliability Engineering International最新文献_第3页

Reliability analysis for manufacturing system of drive shaft based on dynamic Bayesian network 基于动态贝叶斯网络的传动轴制造系统可靠性分析

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3644

Taotao Cheng, Diqing Fan, Xintian Liu, JinGang Wang

引用次数: 0

New CUSUM and EWMA charts with simple post signal diagnostics for two‐parameter exponential distribution 新的 CUSUM 和 EWMA 图表，具有针对双参数指数分布的简单信号后诊断功能

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-14 DOI: 10.1002/qre.3636

Waqas Munir, Abdul Haq

引用次数: 0

Bayesian inference for two populations of Lomax distribution under joint progressive Type‐II censoring schemes with engineering applications 联合渐进式 II 类删减方案下洛马克斯分布两个种群的贝叶斯推断与工程应用

IF 2.2 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-10 DOI: 10.1002/qre.3633

Mustafa M. Hasaballah, Y. Tashkandy, O. S. Balogun, M. E. Bakr

引用次数: 0

Surveillance of high‐yield processes using deep learning models 利用深度学习模型监控高产流程

IF 2.2 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-08 DOI: 10.1002/qre.3635

Musaddiq Ibrahim, Chunxia Zhang, Tahir Mahmood

{"title":"Surveillance of high‐yield processes using deep learning models","authors":"Musaddiq Ibrahim, Chunxia Zhang, Tahir Mahmood","doi":"10.1002/qre.3635","DOIUrl":"https://doi.org/10.1002/qre.3635","url":null,"abstract":"Quality testing and monitoring advancements have allowed modern production processes to achieve extremely low failure rates, especially in the era of Industry 4.0. Such processes are known as high‐yield processes, and their data set consists of an excess number of zeros. Count models such as Poisson, Negative Binomial (NB), and Conway‐Maxwell‐Poisson (COM‐Poisson) are usually considered good candidates to model such data, but the excess zeros are larger than the number of zeros, which these models fit inherently. Hence, the zero‐inflated version of these count models provides better fitness of high‐quality data. Usually, linearly/non‐linearly related variables are also associated with failure rate data; hence, regression models based on zero‐inflated count models are used for model fitting. This study is designed to propose deep learning (DL) based control charts when the failure rate variables follow the zero‐inflated COM‐Poisson (ZICOM‐Poisson) distribution because DL models can detect complicated non‐linear patterns and relationships in data. Further, the proposed methods are compared with existing control charts based on neural networks, principal component analysis designed based on Poisson, NB, and zero‐inflated Poisson (ZIP) and non‐linear principal component analysis designed based on Poisson, NB, and ZIP. Using run length properties, the simulation study evaluates monitoring approaches, and a flight delay application illustrates the implementation of the research. The findings revealed that the proposed methods have outperformed all existing control charts.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926020","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}

引用次数: 0

Gear fault diagnosis based on small channel convolutional neural network under multiscale fusion attention mechanism 多尺度融合关注机制下基于小通道卷积神经网络的齿轮故障诊断

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-06 DOI: 10.1002/qre.3631

Xuejiao Du, Bowen Liu, Jingbo Gai, Yulin Zhang, Xiangfeng Shi, Hailong Tian

{"title":"Gear fault diagnosis based on small channel convolutional neural network under multiscale fusion attention mechanism","authors":"Xuejiao Du, Bowen Liu, Jingbo Gai, Yulin Zhang, Xiangfeng Shi, Hailong Tian","doi":"10.1002/qre.3631","DOIUrl":"https://doi.org/10.1002/qre.3631","url":null,"abstract":"Due to the insufficient feature learning ability and the bloated network structure, the gear fault diagnosis methods based on traditional deep neural networks always suffer from poor diagnosis accuracy and low diagnosis efficiency. Therefore, a small channel convolutional neural network under the multiscale fusion attention mechanism (MSFAM‐SCCNN) is proposed in this paper. First, a small channel convolutional neural network (SCCNN) model is constructed based on the framework of the traditional AlexNet model in order to lightweight the network structure and improve the learning efficiency. Then, a novel multiscale fusion attention mechanism (MSFAM) is embedded into the SCCNN model, which utilizes multiscale striped convolutional windows to extract key features from three dimensions, including temporal, spatial, and channel‐wise, resulting in more precise feature mining. Finally, the performance of the MSFAM‐ SCCNN model is verified using the vibration data of tooth‐broken gears obtained by a self‐designed experimental bench of an ammunition supply and delivery system.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940727","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}

引用次数: 0

Enhancing qualification via the use of diagnostics and prognostics techniques 通过使用诊断和预测技术提高合格率

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-05 DOI: 10.1002/qre.3634

Abhishek Ram, Diganta Das

{"title":"Enhancing qualification via the use of diagnostics and prognostics techniques","authors":"Abhishek Ram, Diganta Das","doi":"10.1002/qre.3634","DOIUrl":"https://doi.org/10.1002/qre.3634","url":null,"abstract":"Qualification is a process that demonstrates whether a product meets or exceeds specified requirements. Testing and data analysis performed within a qualification procedure should verify that products satisfy those requirements, including reliability requirements. Most of the electronics industry qualifies products using procedures dictated within qualification standards. A review of common qualification standards reveals that those standards do not consider customer requirements or the product physics‐of‐failure in that intended application. As a result, qualification, as represented in the reviewed qualification standards, would not meet our definition of qualification for reliability assessment. This paper introduces the application of diagnostics and prognostics techniques to analyze real‐time data trends while conducting qualification tests. Diagnostics techniques identify anomalous behavior exhibited by the product, and prognostics techniques forecast how the product will behave during the remainder of the qualification test and how the product would have behaved if the test continued. As a result, combining diagnostics and prognostics techniques can enable the prediction of the remaining time‐to‐failure for the product undergoing qualification. Several ancillary benefits related to an improved testing strategy, parts selection and management, and support of a prognostics and health management system in operation also arise from applying prognostics and diagnostics techniques to qualification.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940728","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}

引用次数: 0

A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing 基于滚动轴承健康指数构建和可靠性建模的混合可靠性评估方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-04 DOI: 10.1002/qre.3630

Yuan‐Jian Yang, Chengyuan Ma, Gui‐Hua Liu, Hao Lu, Le Dai, Jia‐Lun Wan, Junyu Guo

{"title":"A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing","authors":"Yuan‐Jian Yang, Chengyuan Ma, Gui‐Hua Liu, Hao Lu, Le Dai, Jia‐Lun Wan, Junyu Guo","doi":"10.1002/qre.3630","DOIUrl":"https://doi.org/10.1002/qre.3630","url":null,"abstract":"The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings’ reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940731","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}

引用次数: 0

Advancing software reliability with time series insights: A non‐autoregressive ANN approach 利用时间序列洞察力提高软件可靠性：非自回归 ANN 方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-02 DOI: 10.1002/qre.3632

Shiv Kumar Sharma, Rohit Kumar Rana

{"title":"Advancing software reliability with time series insights: A non‐autoregressive ANN approach","authors":"Shiv Kumar Sharma, Rohit Kumar Rana","doi":"10.1002/qre.3632","DOIUrl":"https://doi.org/10.1002/qre.3632","url":null,"abstract":"Software reliability is a critical factor in assessing the health of software and identifying defects. Software reliability growth models (SRGM) are used to estimate the occurrence of software faults. There are various parameterized and non‐parameterized models of SRGM. These models effectively predict fault occurrence for limited testing conditions. To resolve this problem various neural and artificial neural network (ANN) models are proposed. A problem while using ANN is over‐fitting and under‐fitting. Non‐autoregressive time series models, including ANN variants, offer promising solutions to address under‐fitting issues in SRGM, providing enhanced predictive capabilities for fault occurrence across diverse testing conditions. This study proposes a modified version with a Bayesian regularization technique to address over‐fitting. This modification aims to enhance the suitability of the Bayesian regularization framework for nonlinear autoregressive (NAR) models by carefully adjusting regularization parameters. Comprehensive testing with real‐world software failure datasets is conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that our modified approach improved generalization capabilities and increased prediction accuracy. The NAR‐ANN model exhibits a lower mean squared error of 0.12935 and a higher value of 0.99853.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882349","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}

引用次数: 0

Implementation of compound optimal design in progressive first‐failure censored data 在渐进式首次失败删减数据中实施复合优化设计

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-31 DOI: 10.1002/qre.3628

Vaibhav N. Dhameliya, Raj Kamal Maurya, Ritwik Bhattacharya

引用次数: 0

A weakest link theory‐based probabilistic fatigue life prediction method for the turbine disc considering the influence of the number of critical sections 基于最薄弱环节理论的涡轮盘概率疲劳寿命预测方法，考虑到关键部分数量的影响

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-29 DOI: 10.1002/qre.3629

Tianxing Wang, Yan‐Feng Li, Hong‐Zhong Huang, Song Bai

引用次数: 0