Mantenimiento predictivo
Aplique deep learning al mantenimiento predictivo con Deep Learning Toolbox™ y Predictive Maintenance Toolbox™. Puede entrenar redes neuronales profundas para realizar distintas tareas de mantenimiento predictivo, como detecciones de errores y estimaciones de vida útil restante.
Temas
- Generate Synthetic Signals Using Conditional GAN (Signal Processing Toolbox)
Use a conditional generative adversarial network to produce synthetic signals.
- Chemical Process Fault Detection Using Deep Learning (Predictive Maintenance Toolbox)
Use simulation data to train a neural network than can detect faults in a chemical process.
- Rolling Element Bearing Fault Diagnosis Using Deep Learning (Predictive Maintenance Toolbox)
This example shows how to perform fault diagnosis of a rolling element bearing using a deep learning approach.
- 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. (Desde R2025a)
- 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).
- 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.
- Battery Cycle Life Prediction Using Deep Learning (Predictive Maintenance Toolbox)
Predict the remaining cycle-life of a fast charging Li-ion battery by training a deep neural network.
- Detect Unbalanced Motor by Using Neural Network (Motor Control Blockset)
This example shows how to detect a mechanically unbalanced spinning motor by using a neural network (NN) developed using Deep Learning Toolbox™.
