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.
- 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.