Machine learning for preventive maintenance of mining haul trucks

Description

Building machine learning solutions means a lot more than just training models from a neatly organized dataset magically provided to you. Creating preventive maintenance AI is no exception.

Supervised learning technique for haul trucks’ preventive maintenance involves high-dimensional inputs, complex reasons for truck failures, and very few truck failure samples. Having a large volume of data with a low number of target instances creates the danger of poor model generalization, either from lack of model complexity or from model overfitting. Using this example, Alex Gorbachev and Paul Spiegelhalter explain how to map preventive maintenance needs to supervised machine learning problems, create labeled datasets, do feature engineering from sensors and alerts data, evaluate models—then convert it all to a complete AI solution on Google Cloud Platform that’s integrated with existing on-premises systems. Join in to learn strategies for dealing with feature engineering in situations where you have complex inputs in high volume, complex situations to model, and very few labeled targets to work with.

Topics include:

• Expressing needs to predict failures and recommend maintenance as supervised machine learning problems
• Typical data sources needed for success
• Turning historical data into training and testing examples
• Practical labeling strategies
• Feature engineering approaches for a large number of input sensors and alarms
• Disciplined performance evaluation