Applications of AI for Predictive Maintenance

In this workshop, you’ll learn how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions. You’ll learn how to prepare time-series data for AI model training, develop an XGBoost ensemble tree model, build a deep learning model using a long short-term memory (LSTM) network, and create an autoencoder that detects anomalies for predictive maintenance. At the end of the workshop, you’ll be able to use AI to estimate the condition of equipment and predict when maintenance should be performed.

• Use AI-based predictive maintenance to prevent failures and unplanned downtimes
• Identify key challenges around detecting anomalies that can lead to costly breakdowns
• Use time-series data to predict outcomes with XGBoost-based machine learning classification models
• Use an LSTM-based model to predict equipment failure
• Use anomaly detection with time-series autoencoders to predict failures when limited failure-example data is available

Experienced Python Developers

• Experience with Python
• Basic understanding of data processing and deep learning