Course Overview
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 use this information to map anomalies to failure conditions. You’ll be able to leverage predictive maintenance to manage failures and avoid costly unplanned downtimes. To begin, you’ll learn the key challenges around identifying anomalies that can lead to costly breakdowns. We’ll discuss how you can leverage your company’s time-series data to predict outcomes using machine learning classification models with XGBoost. Then, you’ll learn how to apply predictive maintenance procedures by using an LSTM-based model to predict the failure of a device and avoid downtime. Finally, you will experiment with autoencoders to detect anomalies by using the time series sequences from the previous steps. At the conclusion of the workshop, you’ll learn how to:
- Predict part failures using machine learning classification models with XGBoost
- Train GPU LSTM-based models using Keras and TensorFlow for failure prediction in time series
- Detect anomalies using an autoencoder and Seq2Seq models
- Experiment with generative adversarial network (GAN) models to detect anomalies
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Certifications
This course is part of the following Certifications:
Prerequisites
- Experience with Python
- Basic understanding of data processing and deep learning
Suggested materials to satisfy prerequisites: Python Tutorial, Getting Started with Deep Learning
Course Objectives
- 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
Outline: Applications of AI for Predictive Maintenance (AAPM)
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Training XGBoost Models with RAPIDS for Time Series
- Learn how to predict part failures using XGBoost classification on GPUs with cuDF:
- Prepare real data for efficient GPU ingestion with RAPIDS cuDF.
- Train a classification model using GPU-accelerated XGBoost and CPU-only XGBoost.
- Compare and discuss performance and accuracy results for XGBoost using CPUs, GPUs, and GPUs with cuDF.
Training LSTM Models Using Keras and TensorFlow for Time Series
- Learn how to predict part failures using a deep learning LSTM model with time-series data:
- Prepare sequenced data for time-series model training.
- Build and train a deep learning model with LSTM layers using Keras.
- Evaluate the accuracy of the model.
Training Autoencoders for Anomaly Detection
- Learn how to predict part failures using anomaly detection with autoencoders:
- Build and train an LSTM autoencoder.
- Develop and train a 1D convolutional autoencoder.
- Experiment with hyperparameters and compare the results of the models.
Assessment and Q&A