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

A novel structural reliability analysis method combining the improved beluga whale optimization and the arctangent function‐based maximum entropy method 结合改进白鲸优化法和基于反正切函数的最大熵法的新型结构可靠性分析方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3640

Yufeng Wang, Yonghua Li, Dongxu Zhang, Duo Zhang, Min Chai

{"title":"A novel structural reliability analysis method combining the improved beluga whale optimization and the arctangent function‐based maximum entropy method","authors":"Yufeng Wang, Yonghua Li, Dongxu Zhang, Duo Zhang, Min Chai","doi":"10.1002/qre.3640","DOIUrl":"https://doi.org/10.1002/qre.3640","url":null,"abstract":"A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"47 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179112","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

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

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":"57 1","pages":""},"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":"23 1","pages":""},"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":"95 1","pages":""},"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":"43 1","pages":""},"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

A novel reliability analysis approach for multi‐component systems with stochastic dependency and functional relationships 具有随机依赖性和功能关系的多组件系统的新型可靠性分析方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-27 DOI: 10.1002/qre.3621

Karim Atashgar, Majid Abbasi, Mostafa Khazaee, Mehdi Karbasian

{"title":"A novel reliability analysis approach for multi‐component systems with stochastic dependency and functional relationships","authors":"Karim Atashgar, Majid Abbasi, Mostafa Khazaee, Mehdi Karbasian","doi":"10.1002/qre.3621","DOIUrl":"https://doi.org/10.1002/qre.3621","url":null,"abstract":"Reliability prediction for complex systems utilizing prognostic methods has attracted increasing attention. Furthermore, achieving accurate reliability predictions for complex systems necessitates considering the interaction between components and the multivariate functional relationship that exists among them. This paper proposes a bi‐level method to evaluate the variability of degradation processes and predictive reliability based on the profile monitoring approach for multicomponent systems. Firstly, a multivariate profile structure is introduced to model the framework of degradation analysis in scenarios where there exists stochastic dependency and a multivariate functional relationship between the degradation processes of components. At the component level, the objective is to evaluate the variability of the degradation process for each component considering the presence of stochastic dependence. For the system level analysis, the proposed approach enables the prediction of degradation variability and system reliability by considering the functional relationships among components, without the need for direct calculation of individual component reliabilities. The performance of the proposed model is evaluated through a numerical study and sensitivity analysis conducted on a multicomponent system with a k‐out‐of‐n structure. The results demonstrate the model's notable flexibility and efficiency.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"58 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771801","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

Multimode high‐dimensional time series clustering and monitoring for wind turbine SCADA data 风力涡轮机 SCADA 数据的多模式高维时间序列聚类和监测

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-26 DOI: 10.1002/qre.3626

Luo Yang, Kaibo Wang, Jie Zhou

{"title":"Multimode high‐dimensional time series clustering and monitoring for wind turbine SCADA data","authors":"Luo Yang, Kaibo Wang, Jie Zhou","doi":"10.1002/qre.3626","DOIUrl":"https://doi.org/10.1002/qre.3626","url":null,"abstract":"The operating process of complex systems usually manifest in multiple distinct operating modes. In the case of a wind turbine, for example, its operating mode is highly influenced by the wind condition, which changes dynamically in natural environment. The SCADA system plays a crucial role in collecting various parameters from wind turbines, facilitating the differentiation, and modeling of distinct operating modes. However, the challenge lies in the excessive dimensionality of variables in SCADA data, making modeling efforts both intricate and inefficient. In this study, we leverage the engineering knowledge on the hierarchical structure of the variables in wind turbine, and propose a novel method to efficiently cluster the data temporally by operating modes. Our methodology involves initially clustering variables according to subsystems and implementing temporal clustering within each subsystem. Subsequently, we introduce a novel graph neural network to extract and concatenate features from all subsystems, enabling the discrimination of the operational mode of the entire system. Finally, we model these features to make predictions of the output power, and the prediction residual can be used for monitoring. Performance evaluations on both numerical experiments and real‐world wind turbine datasets attest to the effectiveness and superiority of the proposed methods.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"4 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771802","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