主要内容

Preprocess Data

清洁和转换数据以准备它以在命令行和应用中提取条件指标

在用于预测维护的算法设计中，通常需要进行数据预处理以清洁数据并将其转换为可以从中提取条件指标的形式。您可以使用预测性维护Toolbox™集合数据存储器对数组或表的数组或表进行数据预处理。有关某些常见类型的数据预处理的概述，请参见Data Preprocessing for Condition Monitoring and Predictive Maintenance.

TheDiagnostic Feature Designer应用程序使您可以交互性地执行许多预处理操作。应用程序中的处理工具包括过滤，时间域处理，频域处理和插值。应用时域处理选项包括用于旋转机械的专门过滤。有关应用程序的更多信息，请参阅Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer.

Apps

Diagnostic Feature Designer 从测量或模拟数据中进行互动提取，可视化和等级特征，用于机器诊断和预后。

Functions

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fillmissing 填写缺失值
filloutliers Detect and replace outliers in data
smoothdata Smooth noisy data
movmean Moving mean
detrend Remove polynomial trend
恢复 阵列元素的比例范围
筛选 1-D digital filter
designfilt 设计数字过滤器
TSA Time-synchronous signal average
tsAdifference Difference signal of a time-synchronous averaged signal
TSAregular Regular signal of a time-synchronous averaged signal
TSAresidual Residual signal of a time-synchronous averaged signal
ordertrack 跟踪和提取从振动信号中的阶数
rpmtrack Track and extract RPM profile from vibration signal
pspectrum 分析频率和时频域中的信号
Envspectrum 用于机械诊断的信封光谱
orderspectrum 平均光谱与振动信号的顺序
modalfrf Frequency-response functions for modal analysis
bearingFaultBands Generate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction
gearMeshFaultBands Construct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction
faultBands Generate fault frequency bands for spectral feature extraction
五局 Spectral entropy of signal
pkurtosis 信号或光谱图的光谱峰度
kurtogram Visualize spectral kurtosis
spectrogram Spectrogram using short-time Fourier transform
HHT Hilbert-Huang transform
emd Empirical mode decomposition

Topics

Data Preprocessing for Condition Monitoring and Predictive Maintenance

Use signal-processing techniques to preprocess data, cleaning it and converting it into a form from which you can extract condition indicators. Knowledge of your system can help you choose an appropriate preprocessing approach.

Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer

Follow this workflow for interactively exploring and processing ensemble data, designing and ranking features from that data, and exporting data and selected features, and generating MATLAB code.

Organize System Data for Diagnostic Feature Designer

Organize measurements and information for multiple systems into data sets that you can import into the app.

Process Data and Explore Features in Diagnostic Feature Designer

Filter and transform data within the app. Extract features from the imported and derived signals, and assess feature effectiveness.

Featured Examples

Multi-Class Fault Detection Using Simulated Data

Multi-Class Fault Detection Using Simulated Data

Use a Simulink model to generate faulty and healthy data, and use the data to develop a multi-class classifier to detect different combinations of faults.
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分析和选择泵诊断功能

分析和选择泵诊断功能

Use the Diagnostic Feature Designer app to analyze and select features to diagnose faults in a triplex reciprocating pump.
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Wind Turbine High-Speed Bearing Prognosis

Wind Turbine High-Speed Bearing Prognosis

Build an exponential degradation model to predict the Remaining Useful Life (RUL) of a wind turbine bearing in real time. The exponential degradation model predicts the RUL based on its parameter priors and the latest measurements.
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预测维护工具箱Documentation

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用MATLAB进行预测维护

用MATLAB进行预测维护

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