物流瑞利分布质量特性可靠性试验计划的应用

Q1 Decision Sciences Annals of Data Science Pub Date : 2023-07-19 DOI:10.1007/s40745-023-00473-5
Mahendra Saha, Harsh Tripathi, Anju Devi, Pratibha Pareek
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引用次数: 0

摘要

本文考虑了时间截断寿命试验下的可靠性试验计划,即 logistic Rayleigh 分布(\(\mathcal {LRD}\))。本文简要讨论了 \(\mathcal {LRD}\) 的统计特性和意义。中值越大,批次质量越好,这被认为是建议的可靠性测试计划的质量特征。针对不同的消费者风险设置,最小样本量以表格形式列出。此外,还以表格形式显示了所选设置的运行特征(\(\mathcal{O}\mathcal{C}\))值,并讨论了\(\mathcal{O}\mathcal{C}\)值的模式。根据样本量,讨论了本研究与其他一些可靠性测试计划的比较分析。作为说明,通过实际例子展示了所建议的计划在 \(\mathcal {LRD}\) 方面的性能。
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Applications of Reliability Test Plan for Logistic Rayleigh Distributed Quality Characteristic

In this article, a reliability test plan under time truncated life test is considered for the logistic Rayleigh distribution (\(\mathcal {LRD}\)). A brief discussion over statistical properties and significance of the \(\mathcal {LRD}\) is placed in this present study. Larger the value of median—better is the quality of the lot is considered as quality characteristic for the proposed reliability test plan. Minimum sample sizes are placed in tabular form for different set up of specified consumer’s risk. Also operating characteristics (\(\mathcal{O}\mathcal{C}\)) values are shown in tabular forms for the chosen set up and discussed the pattern of \(\mathcal{O}\mathcal{C}\) values. A comparative analysis of the present study with some other reliability test plans is discussed based on the sample sizes. As an illustration, the performance of the proposed plan for the \(\mathcal {LRD}\) is shown through real-life examples.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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