Industrial Data Analytics for Diagnosis and Prognosis by Shiyu Zhou, Yong Chen
Requirements: .ePUB reader, 10 MB
Overview: Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model
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.
Genre: Non-Fiction > Educational
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