ml-pse / machine_learning_for_pm_and_pdm

Code repository for the book 'Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance'

Machine_Learning_for_PM_and_PdM

Code repository for the book 'Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance'

Book Links:

• Google Play: https://play.google.com/store/books/details?id=7JXtEAAAQBAJ
• LeanPub: https://leanpub.com/ML-Python-for-PM-PdM

Data sources for datasets used in this book:

[Weblinks mentioned below may change or may no longer exist in future. Relevant data files have been provided in the respective folders in this repository. If you plan to share or use any dataset, please abide by the license policy (and/or the citation requests, if any) for the dataset.]

• Aeration Tank Data:

   Publicly available at https://openmv.net/info/aeration-rate.
  

• Steam Generator Data:

   Obtained from https://homes.esat.kuleuven.be/~smc/daisy/daisydata.html’
   
   Citation: De Moor B.L.R. (ed.), DaISy: Database for the Identification of Systems, Department of Electrical Engineering, ESAT/STADIUS, KU Leuven, Belgium, URL: http://homes.esat.kuleuven.be/~smc/daisy/.
  

• Polymer Manufacturing Process Data:

   Originally obtained from https://landing.umetrics.com/downloads-other-downloads (unfortunately this link no longer seems to work; data file is provided in the respective folder in this repository). 
   Dataset also referenced at https://www.academia.edu/38630159/Multivariate_data_analysis_wiki
  

• Low-Density Polyethylene (LDPE) Process Data:

   Obtained from https://openmv.net.
  

• Tennessee Eastman Process Data:

   Available at https://github.com/camaramm/tennessee-eastman-profBraatz (Copyright (c) 1998-2002 The Board of Trustees of the University of Illinois).
  

• Semiconductor Manufacturing Process Data:

   Obtained from http://www.eigenvector.com/data/Etch/. 
   
   Citation: B.M. Wise, N.B. Gallagher, S.W. Butler, D.D. White, Jr. and G.G. Barna, "A Comparison of Principal Components Analysis, Multi-way Principal Components analysis, Tri-linear Decomposition and Parallel Factor Analysis for Fault Detection in a Semiconductor Etch Process", J. Chemometrics (1999).
  

• Gas Boiler Data:

   Shohet et al., Simulated boiler data for fault detection and classification. Available at https://ieee-dataport.org/open-access/simulated-boiler-data-fault-detection-and-classification, IEEE Dataport, 2019.
  
   Data shared under Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/).
  

• Debutanizer Column Data from a Petroleum Refinery:

   Available as supplementary material at https://link.springer.com/book/10.1007/978-1-84628-480-9. 
   
   Citation: Fortuna et. al., Soft sensors for monitoring and control of industrial processes, Springer, 2007
  

• Combined Cycle Power Plant Data:

   Available at the UCI machine learning repository https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant
   
   Citation: Pınar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615
  

• Fluid Catalytic Cracking Unit Data:

   Details available at https://mlforpse.com/fccu-dataset/.
  

• Wind Turbine Data:

    Available at https://github.com/mathworks/WindTurbineHighSpeedBearingPrognosis-Data. Data has been shared by MathWorks under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Permission was granted by the original author of the dataset, Eric                Bechhoefer, to use the data in this book.
  

• Gas Turbine Data:

   Originally available at NASA prognostics data repository https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/. Data available at https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/about_data.
   Training and validation data file names used in the text are different than the original file names. 
   
   Citation: A. Saxena and K. Goebel (2008). "Turbofan Engine Degradation Simulation Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA