Practical Electrical and Current Signature Analysis of Electrical Machinery and Systems
eBook - PDF

Practical Electrical and Current Signature Analysis of Electrical Machinery and Systems

  1. 243 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Practical Electrical and Current Signature Analysis of Electrical Machinery and Systems

About this book

Written with the field technician, engineer, or reliability/maintenance manager in mind, Practical Electrical and Current Signature Analysis for Electrical Machinery and Systems is an essential working tool for the new and experienced ESA and MCSA practitioner. The book takes the practitioner well beyond the electric motor into incoming power, mechanical components, and driven equipment up to and including complete DFIG wind turbine and powertrain analysis and alternative energy transformers. It is based upon decades of field experience and practical research concluding with an overview of machine learning associated with basic motor data and present applications of ESA/MCSA IoT systems. Whether data collection or continuous monitoring regardless of the technician's choice of technology, you will understand the requirements, capabilities, and limitations to provide accurate prognostics. For managers and engineers who need to understand the capabilities of ESA/MCSA the book provides an easy-to-understand overview of the technology along with extensive real case studies.

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Yes, you can access Practical Electrical and Current Signature Analysis of Electrical Machinery and Systems by Howard Penrose in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Civil Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Copyright
  3. Contents
  4. Table of Equations
  5. Table of Figures
  6. Forward
  7. Section 1: Power Quality and Power Supply
  8. Section 2: Electric Machines and ESA
  9. Section 3: Evaluation of Driven Equipment with ESA
  10. Section 4: Tying it Together in a Wind Turbine
  11. Section 5: Machine Learning and RUL/TTFE
  12. Index