AI in Predictive Maintenance
Apply deep learning and machine learning to predictive maintenance by using Deep Learning Toolbox™ or Statistics and Machine 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. You can use classification and regression techniques to assess feature effectiveness and create deployable models.
Detect and Diagnose Faults
- 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.
- Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data
Detect anomalies in industrial-machine vibration data using machine learning and deep learning.
Predict Remaining Useful Life
- Battery Cycle Life Prediction From Initial Operation Data
Predict the remaining cycle-life of a fast charging Li-ion battery using a supervised machine learning algorithm.
- Remaining Useful Life Estimation Using Convolutional Neural Network
This example shows how to predict the RUL of engines using deep convolutional neural networks (CNN).
- 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.