Applications of AI for Predictive Maintenance Training

• Experience with Python; basic understanding of data processing and deep learning.
• To gain experience with Python, we suggest QA's Practical Machine Learning course.
• To get a basic understanding of data processing and deep learning, we suggest DLI’s Fundamentals of Deep Learning for Computer Vision.

In this workshop, developers will learn how to:

• 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

Why DLI Hands-On Training?

• Build deep learning, accelerated computing, and accelerated data science applications for industries such as autonomous vehicles, healthcare, manufacturing, media and entertainment, robotics, smart cities, and more.
• Gain real-world expertise through content designed in collaboration with industry leaders, such as the Children’s Hospital of Los Angeles, Mayo Clinic, PwC, and Uber.
• Access content anywhere, anytime with a fully configured, GPU-accelerated server in the cloud.
• Earn an NVIDIA DLI certificate to demonstrate subject matter competency and support career growth.
• Work with the most widely used, industry-standard software, tools, and frameworks.

Introduction 

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.

Related Training

If your organization is interested in using AI to detect defects in manufacturing equipment, we recommend the instructor-led workshop Applications of AI for Anomaly Detection. Your team will learn how to apply multiple techniques across accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) to detect anomalies, specifically to identify network intrusions.

Additional Resources

DLI offers other hands-on training and educational resources on AI for manufacturing, including:

• Self-paced, online courses on deep learning, accelerated computing, and accelerated data science at www.nvidia.com/dli
• Instructor-led workshops on deep learning for anomaly detection, industrial inspection, multi-GPUs, and more at www.nvidia.com/dli
• Blogs, webinars, and other resources on AI for manufacturing at www.nvidia.com/industrial