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.
Highlights
Data driven decision-making may be defined by the set of practices aiming to make decisions based on data analysis rather than on intuitive insights [1]
The success of Artificial Intelligence (AI) based tools is mainly due to the advances in machine learning approaches [4]. This is stimulated by the availability of large datasets concerning various real-world features [3] and through the increase of the computational gains which are generally attributed to the powerful GPU cards [5]
We considered the works dealing with transparency and explainability of automated machine learning and those related to big industrial data mining
Summary
Data driven decision-making may be defined by the set of practices aiming to make decisions based on data analysis rather than on intuitive insights [1]. The manufacturing area is one of those generating huge amounts of data gathered by means of Cyber Physical System (CPS) devices The availability of such data combined with the knowledge of manufacturing experts may be an opportunity to build AI based processes and models providing high value insights and assets for decision makers. It is a preliminary objective of the current work to make the outcome from such well-performing AutoML systems transparent, interpretable and selfexplainable This shall make AutoML support systems more reliable and operational through a set of different visual summary levels of the provided models and configurations. It may render the AutoML system more transparent and controllable, increasing its acceptance.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
