用于诊断和预后的工业数据分析:随机效应建模方法

IF 2.6 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL
Jing Li
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引用次数: 2

摘要

在《用于诊断和预测的工业数据分析:随机效应建模方法》一书中,杰出的工程师周世宇和陈勇对用于工业系统诊断和预测的随机效应建模方法进行了严格而实用的介绍。在书的两个部分,一般的统计概念和有用的理论被描述和解释,因为是工业诊断和预后方法。完成的作者描述和模型固定效应,随机效应,和变化在单变量和多变量数据集,并涵盖随机效应方法的应用,以诊断变化源在工业过程。在将随机效应方法应用于工业过程和系统的故障预测之前,他们提供了不同诊断方法的详细性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Industrial Data Analytics for Diagnosis and Prognosis: A Random Effects Modeling Approach
In Industrial Data Analytics for Diagnosis and Prognosis A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover the application of the random effects approach to diagnosis of variation sources in industrial processes. They offer a detailed performance comparison of different diagnosis methods before moving on to the application of the random effects approach to failure prognosis in industrial processes and systems.
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来源期刊
Journal of Quality Technology
Journal of Quality Technology 管理科学-工程:工业
CiteScore
5.20
自引率
4.00%
发文量
23
审稿时长
>12 weeks
期刊介绍: The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers. Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days
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