什么是预测维护工具箱?
预测维护工具箱™ provides capabilities for estimating the remaining useful life (RUL) of a machine and extracting features to design condition indicators which can help monitor the health of a machine. The toolbox also provides capabilities for managing and labeling data, as well as reference examples for developing algorithms for bearings, pumps, batteries, and other machines.
The Predictive Maintenance Toolbox™ provides capabilities and reference examples for designing and testing condition monitoring and predictive maintenance algorithms for ball bearings, pumps, batteries, and other machines.
使用诊断功能设计器从传感器数据中提取功能,而无需编写任何MATLAB®code. Filter and preprocess sensor data signals and extract time domain features such as mean and standard deviation. You can also estimate a signal’s power and order spectra and extract frequency domain features such as spectral peak values. After you have computed your features, you can plot and rank them to determine which features are best suited for your fault classification and remaining useful life algorithms, and export them.
You can estimate the time to failure of your machine or its remaining useful life using similarity methods which require run-to-failure data, survival methods—which require lifetime data related to events such as part replacement and part failure—and trend-based methods, which require a known failure threshold.
As you can see, the methods also provide confidence intervals for the predictions made.
Every algorithm needs data, and you can import yours from the cloud, HDFS, and local files before organizing it in MATLAB. If you don’t have any failure data, you can generate simulation data from Simulink®结合故障条件的机器型号。
该文档和示例可以帮助您开始介绍算法开发过程的工作流程。
For more information on the Predictive Maintenance Toolbox, please return to the product page.
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