Predictive Maintenance

Accelerate predictive maintenance applications with parallel computing

Use parallel computing to accelerate predictive maintenance applications by using Parallel Computing Toolbox™ together with Predictive Maintenance Toolbox™.

Topics

Accelerate Fault Diagnosis Using GPU Data Preprocessing and Deep Learning (Predictive Maintenance Toolbox)
This example shows how to use GPU computing to accelerate data preprocessing and deep learning for predictive maintenance workflows. (Since R2025a)
Detect Anomalies in Industrial Machinery Using Three-Axis Vibration Data (Predictive Maintenance Toolbox)
Detect anomalies in industrial machine vibration data using machine-learning and deep-learning models trained with data representing only nominal behavior.
Detect Aging Severity in Power Converters (Predictive Maintenance Toolbox)
Generate synthetic semiconductor degradation data from a power converter model, and use that data to build a predictive maintenance algorithm that can detect aging severity in a power converter.
Remaining Useful Life Estimation Using Convolutional Neural Network (Predictive Maintenance Toolbox)
This example shows how to predict the RUL of engines using deep convolutional neural networks (CNN).

Related Information

Functions with gpuArray Support (Predictive Maintenance Toolbox)
Functions with Automatic Parallel Support (Predictive Maintenance Toolbox)

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