{"product_id":"industrial-data-analytics-for-diagnosis-and-prognosis-9781119666288","title":"Industrial Data Analytics for Diagnosis and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDiscover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn\u003ci\u003eIndustrial Data Analytics for Diagnosis and Prognosis -A Random Effects Modelling Approach\u003c\/i\u003e, distinguished engineers Shiyu Zhou and Yong Chen delivera 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 datasetsand 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 methodsbefore moving on to the application of the random effects approach to failure prognosis in indu\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 1 Introduction\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Statistical Methods and Foundation for Industrial Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 2 Introduction to Data Visualization andChapteraracterization\u003c\/p\u003e \u003cp\u003eChapter 3 Random Vectors and the Multivariate Normal Distribution\u003c\/p\u003e \u003cp\u003eChapter 4 Explaining Covariance Structure: Principal Components\u003c\/p\u003e \u003cp\u003eChapter 5 Linear Model for Numerical and Categorical\u003c\/p\u003e \u003cp\u003eChapter 6 Linear Mixed Effects Model\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Random Effects Approaches for Diagnosis and Prognosis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 7 Diagnosis of Variation Source Using PCA\u003c\/p\u003e \u003cp\u003eChapter 8 Diagnosis of Variation Sources Through Random Effects Estimation\u003c\/p\u003e \u003cp\u003eChapter 9 Analysis of System Diagnosability\u003c\/p\u003e \u003cp\u003eChapter 10 Prognosis Through Mixed Effects Models for Longitudinal Data\u003c\/p\u003e \u003cp\u003eChapter 11 Prognosis Using Gaussian Process Model\u003c\/p\u003e \u003cp\u003eChapter 12 Prognosis Through Mixed Effects Models for Time-to-Event Data\u003c\/p\u003e \u003cp\u003eAppendix: Basics of Vectors, Matrices, and Linear Vector Space\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407113429335,"sku":"9781119666288","price":101.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119666288.jpg?v=1730498229","url":"https:\/\/bookcurl.com\/products\/industrial-data-analytics-for-diagnosis-and-prognosis-9781119666288","provider":"Book Curl","version":"1.0","type":"link"}