Main Content

Get Started with预测维护工具箱

Design and test condition monitoring and predictive maintenance algorithms

预测维护工具箱™ lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine.

该工具箱提供了使用基于数据和模型的技术（包括统计，光谱和时间序列分析）的功能和用于探索，提取和排名功能的交互式应用程序。您可以使用频率和时频方法从振动数据中提取功能来监视旋转机的健康。为了估算机器的失败时间，您可以使用生存，相似性和基于趋势的模型来预测RUL。

您可以组织和分析从本地文件，云存储和分布式文件系统中导入的传感器数据。您可以标记Simulink生成的模拟失败数据金宝app®models. The toolbox includes reference examples for motors, gearboxes, batteries, and other machines that can be reused for developing custom predictive maintenance and condition monitoring algorithms.

To operationalize your algorithms, you can generate C/C++ code for deployment to the edge or create a production application for deployment to the cloud.

Tutorials

About Condition Monitoring and Predictive Maintenance

Videos

Predictive Maintenance Part 1: Introduction
Learn about different maintenance strategies and predictive maintenance workflow. Predictive maintenance lets you find the optimum time to schedule maintenance by estimating time to failure.

预测维护第2部分：用于识别条件指标的功能提取
Learn how to extract condition indicators from your data. Condition indicators help you distinguish between healthy and faulty states of a machine.

Predictive Maintenance Part 3: Remaining Useful Life Estimation
Predictive maintenance lets you estimate the remaining useful life (RUL) of your machine. Explore three common models to estimate RUL: similarity, survival, and degradation

预测维护第4部分：如何使用诊断功能设计器进行功能提取
学习如何提取时域和频谱features using Diagnostic Feature Designer for developing your predictive maintenance algorithm.

预测维护工具箱Documentation

金宝app

Predictive Maintenance with MATLAB

Predictive Maintenance with MATLAB

Download ebook