Predictive Maintenance

Extend deep learning workflows with predictive maintenance applications

Apply deep learning to predictive maintenance by using Deep Learning Toolbox™ together with Predictive Maintenance Toolbox™. You can train deep neural networks to perform various predictive maintenance tasks, such as fault detection and remaining useful life estimation.

Topics

Generate Synthetic Signals Using Conditional GAN
Use a conditional generative adversarial network to produce synthetic data for model training.
Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process.
Rolling Element Bearing Fault Diagnosis Using Deep Learning
This example shows how to perform fault diagnosis of a rolling element bearing using a deep learning approach.
Remaining Useful Life Estimation Using Convolutional Neural Network
This example shows how to predict the RUL of engines using deep convolutional neural networks (CNN).
Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data
Detect anomalies in industrial-machine vibration data using machine learning and deep learning.
Battery Cycle Life Prediction Using Deep Learning
Predict the remaining cycle-life of a fast charging Li-ion battery by training a deep neural network.