preprocess data -凯发k8网页登录

clean and transform data to prepare it for extracting condition indicators at the command line and in the app

in algorithm design for predictive maintenance, data preprocessing is often necessary to clean the data and convert it into a form from which you can extract condition indicators. you can perform data preprocessing on arrays or tables of measured or simulated data that you manage with predictive maintenance toolbox™ ensemble datastores. for an overview of some common types of data preprocessing, see .

the diagnostic feature designer app lets you perform many preprocessing operations interactively. the processing tools in the app include filtering, time-domain processing, frequency-domain processing, and interpolation. app time-domain processing options include specialized filtering for rotating machinery. for more information on the app, see .

apps

diagnostic feature designer

interactively extract, visualize, and rank features from measured or simulated data for machine diagnostics and prognostics

functions

basic preprocessing

	fill missing entries
	detect and replace outliers in data
	smooth noisy data
	moving mean
	remove polynomial trend
	scale range of array elements

filtering

	1-d digital filter
	design digital filters

time-domain preprocessing

	time-synchronous signal average
	difference signal of a time-synchronous averaged signal
	regular signal of a time-synchronous averaged signal
	residual signal of a time-synchronous averaged signal
	track and extract order magnitudes from vibration signal
	track and extract rpm profile from vibration signal

frequency-domain preprocessing

	analyze signals in the frequency and time-frequency domains
	envelope spectrum for machinery diagnosis
	average spectrum versus order for vibration signal
	frequency-response functions for modal analysis
	generate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction
	construct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction
	generate fault frequency bands for spectral feature extraction

time-frequency preprocessing

	spectral entropy of signal
	spectral kurtosis from signal or spectrogram
	visualize spectral kurtosis
	spectrogram using short-time fourier transform
	hilbert-huang transform
	empirical mode decomposition

topics

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.
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 measurements and information for multiple systems into data sets that you can import into the app.
filter and transform data within the app. extract features from the imported and derived signals, and assess feature effectiveness.

featured examples

analyze and select features for pump diagnostics

use the diagnostic feature designer app to analyze and select features to diagnose faults in a triplex reciprocating pump.

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.