Metro de Madrid Adopts Machine Learning for Predictive Maintenance in Tunnels
“We have created a degradation model of the catenary that allows us to anticipate and optimize the maintenance actions.”
Every day, Metro de Madrid stores more than 10 GB of new data acquired from different sources. Many available tools can only analyze data from a single sensor, and such approaches lack domain expertise. In order to use all the data they acquire for predictive maintenance, Metro de Madrid needed to integrate the data from a wide variety of sensors and customize their signal analysis algorithms.
Metro de Madrid used MATLAB® and Statistics and Machine Learning Toolbox™ to automate the data merging, signal analysis, and algorithm sharing, which enables people without MATLAB experience to perform advanced signal analysis.
Key Outcomes
- Saved time in the data validation and analysis phase
- Integrated data from different sources
- Shared algorithms with non-MATLAB users
