Abstract #

Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence (AI) models, but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameters configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field.

Citation #

@article{Garouani2022a,
  doi = {10.1007/s00170-022-08761-9},
  url = {https://doi.org/10.1007/s00170-022-08761-9},
  year = {2022},
  month = feb,
  volume = {120},
  number = {1-2},
  pages = {1169--1188},
  author = {Moncef Garouani and Adeel Ahmad and Mourad Bouneffa and Mohamed Hamlich and Gregory Bourguin and Arnaud Lewandowski},
  title = {Towards big industrial data mining through explainable automated machine learning},
  journal = {The International Journal of Advanced Manufacturing Technology}
}
}