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Machine Learning
Una plataforma propia para algoritmos de aprendizaje automático

Development of predictive maintenance algorithms​

The goal is to demonstrate the potential of the use of machine learning techniques (a branch of artificial intelligence) for the predictive maintenance of rolling stock and in the railway sector in general. The phases defined are as follows:

o             Development of predictive maintenance algorithms, in regard to the use case, based on a learning model that allows the prediction of the main parameters necessary for maintenance.

o             Incorporation of available historical information of the use case, in a high-performance technological platform that allows the learning of the designed algorithms.

o             Application of the resulting learning algorithms to the actual maintenance activity to predict, in real time as much as possible, behaviour of the parameters evaluated. This activity will be carried out in parallel with the existing methods in order to be able to validate the new method.

Data Sheet
Location: Spain
Client: Ineco
Execution period: 2017
Market: Planning

New technologies

The project develops and validates Ineco's Predictive Maintenance platform, which includes machine learning techniques in the railway field and more specifically in rolling stock.

Ineco's development of machine learning algorithms for predictive maintenance means that our clients can minimise the risk of failures, while also avoiding unnecessary preventive maintenance, which greatly increases savings and improves the efficiency of the system.