1478. Integrated optimization and machine learning: an application to predictive maintenance
Invited abstract in session MA-38: Automating the Design, Generation and Control of Optimization Algorithms 1, stream Data Science meets Optimization.
Monday, 8:30-10:00Room: Michael Sadler LG19
Authors (first author is the speaker)
| 1. | Marie Bouilloud
|
| CGS (Centre de Gestion Scientifique), Mines Paris-PSL | |
| 2. | Michel Nakhla
|
| CGS (Centre de Gestion Scientifique), Mines Paris - PSL |
Abstract
This paper examines how optimization and machine learning can be combined to improve predictive maintenance in wind farms.
Optimization integrated with machine learning creates powerful methods to solve complex problems by improving solution search strategies and enhancing decision-making processes, in contrast with the usual sequential approach. This synergy enables algorithms to learn from data and guide optimization procedures more effectively. In this proposal, we present an application of this combination for predictive maintenance, where a MIP schedules tasks to minimize downtime while maximizing system reliability and machine learning models, including random forrest and isolation forrest, anticipate equipment failures.
The model we propose mobilizes ensemble learning with a loop such that the performance of the corresponding tasks planning allows to adjust the threshold for aggregating the results of the different prediction models. The predictions are adjusted through iterations by the results of the optimization. The optimization then leads to a return to the ensemble learning, becoming similar to a layer of a learning algorithm.
Experimentations were carried out on industrial data.
Keywords
- Programming, Mixed-Integer
- Scheduling
- Machine Learning
Status: accepted
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