This library can be used to identify intervals suitable for System Identification scanning historical datasets of industrial processess.
How to reference this work:
Please see the References section to verify the related works.
This library is licensed under the MIT license.
pip3 install HDSIdent
One can find code examples in the notebooks/ folder.
There are two main ways of reproducing the provided notebooks:
-
All the notebooks were created in Google's Colaboratory. Therefore, you can just open the desired notebook in GitHub and click on "Open in Colab".
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You can download the notebook and run it locally (don't forget to install the library through
pip install HDSIdent
)
HDSIdent uses the following open-source libraries:
HDSIdent implements and unifies the methods proposed in the following works:
PETTITT, A.N., 1979. A non-parametric approach to the
change-point problem. Appl. Stat. 28, 126–135.
PERETZKI, D. et al. Data mining of historic data for process identification.
In: Proceedings of the 2011 AIChE Annual Meeting, p. 1027–1033, 2011.
SHARDT, Y. A. W.; SHAH, S. L. Segmentation Methods for Model Identification from
Historical Process Data. In: Proceedings of the 19th World Congress.
Cape Town, South Africa: IFAC, 2014. p. 2836–2841.
BITTENCOURT, A. C. et al. An algorithm for finding process identification
intervals from normal operating data. Processes, v. 3, p. 357–383, 2015.
RIBEIRO, A. H.; AGUIRRE, L. A. Selecting transients automatically
for the identification of models for an oil well. IFAC-PapersOnLine,
v. 48, n. 6, p. 154–158, 2015.
PATEL, A. Data Mining of Process Data in Mutlivariable Systems.
Degree project in electrical engineering — Royal Institute of Technology,
Stockholm, Sweden, 2016.
ARENGAS, D.; KROLL, A. A Search Method for Selecting Informative Data in Predominantly
Stationary Historical Records for Multivariable System Identification.
In: Proceedings of the 21st International Conference on System Theory,
Control and Computing (ICSTCC). Sinaia, Romenia: IEEE, 2017a. p. 100–105.
ARENGAS, D.; KROLL, A. Searching for informative intervals in predominantly stationary
data records to support system identification. In: Proceedings of the XXVI International
Conference on Information, Communication and Automation Technologies (ICAT). Sarajevo,
Bosnia-Herzegovina: IEEE, 2017b.
WANG, J. et al. Searching historical data segments for process
identification in feedback control loops. Computers and Chemical
Engineering, v. 112, n. 6, p. 6–16, 2018.
The following works are also considered:
FACELI, K. et al. Inteligência Artificial: Uma Abordagem de Aprendizado de
Máquina. Rio de Janeiro, Brasil: LTC, 2017. (In portuguese)
AGUIRRE, L. A. Introdução à Identificação de Sistemas:
técnicas lineares e não lineares: teoria e aplicação. 4. ed.
Belo Horizonte, Brasil: Editora UFMG, 2015.
SMITH, S. W. Digital Signal Processing. San Diego, California:
California Technical Publishing, 1999.