Overview

According to the International Society of Automation, $647 billion is lost globally each year due to downtime from machine failure. Organizations across manufacturing, aerospace, energy, and other industrial sectors are overhauling maintenance processes to minimize costs and improve efficiency.

With artificial intelligence (AI) and machine learning, organizations can apply predictive maintenance to their operation, processing huge amounts of sensor data to detect equipment failure before it happens. Compared to routine-based or time-based preventative maintenance, predictive maintenance gets ahead of the problem and can save a business from costly downtime.

In this Deep Learning Institute (DLI) workshop, developers will learn how to identify anomalies and failures in timeseries data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.

They’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, developers will be able to use AI to estimate the condition of equipment and predict when maintenance should be performed.

All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLIcertified instructor, and the opportunity to network with other developers, data scientists, and researchers attending the workshop. Attendees can earn a certificate to prove subject matter competency and support professional growth.

Technologies: Python, TensorFlow, Keras, XGBoost, NVIDIA RAPIDS™, cuDF, LSTM, autoencoders, artificial intelligence, deep learning.

Read more

Prerequisites

Read more

Learning Outcomes

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.
Read more

Course Outline

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
Read more

Why choose QA

Frequently asked questions

See all of our FAQs

How can I create an account on myQA.com?

There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.

If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".

If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.

Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.

How do QA’s virtual classroom courses work?

Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.

We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting (more details in the link below) to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.

Learn more about our Virtual Classrooms.

How do QA’s online courses work?

QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.

All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.

Learn more about QA’s online courses.

When will I receive my joining instructions?

Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.

When will I receive my certificate?

Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.

Contact Us

Please contact us for more information