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.