控制系统

R2026b

通过并行计算加速预测性维护和强化学习应用程序

结合使用 Parallel Computing Toolbox™、Predictive Maintenance Toolbox™ 和 Reinforcement Learning Toolbox™，利用并行计算来加速预测性维护和强化学习应用程序。

主题

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. (自 R2025a 起)
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).

Train Agents Using Parallel Computing and GPUs (Reinforcement Learning Toolbox)
Accelerate agent training by running simulations in parallel on multiple cores, GPUs, clusters or cloud resources.
Train AC Agent to Balance Discrete Cart-Pole Using Parallel Computing (Reinforcement Learning Toolbox)
Train an AC agent to control a discrete action space cart-pole system using asynchronous parallel computing.
Train DQN Agent for Lane Keeping Assist Using Parallel Computing (Reinforcement Learning Toolbox)
Train a DQN agent for an automated driving application using parallel computing.