manage system data -凯发k8网页登录

import measured data, generate simulated data, organize data for use at the command line and in the app

data analysis is the heart of condition monitoring and predictive maintenance. designing algorithms for predictive maintenance requires organizing and analyzing large amounts of data while keeping track of the systems and conditions the data represents.

predictive maintenance toolbox™ provides tools for managing sensor data stored locally and remotely, as well as for generating simulated data by running a simulink^® model. the main unit for organizing and managing multifaceted data sets in predictive maintenance toolbox is an ensemble. an ensemble is a collection of data sets, created by measuring or simulating a system under varying conditions. manage your ensemble using ensemble datastore objects. for more information about how ensembles work and how to use them, see .

the diagnostic feature designer app includes interactive tools for processing data and extracting features. the app accepts data sets in various forms, consolidates the data within the app, and manages that data internally during a session. for more information on the app, see .

objects

	manage ensemble data in custom file format
	manage ensemble data generated by `generatesimulationensemble` or by logging simulation data in simulink
	manage ensemble data stored in the matlab workspace using code generated by diagnostic feature designer

functions

	generate ensemble data by running a simulink model
	read member data from an ensemble datastore
	write data to member of an ensemble datastore
	determine if data is available to read
	reset datastore to initial state
	create new ensemble datastore from subset of existing ensemble datastore
	number of datastore partitions
	partition a datastore
	determine how much data has been read
	create tall array

topics

ensemble datastore basics

algorithm design with predictive maintenance toolbox uses data organized in ensembles. you can generate ensemble data from a simulink model or create ensembles from existing data stored on disk.
generate and use simulated data ensemble
if you have a simulink model of your system under fault conditions, you can generate an ensemble of simulated data for developing predictive-maintenance algorithms.
use a file ensemble datastore to manage and interact with large sets of data collected from operation of your system under varying conditions.
create and use a fileensembledatastore object to manage an ensemble of data stored in a plain-text format.

data management in the 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 measurements and information for multiple systems into data sets that you can import into the app.
import an ensemble member table from your matlab workspace, define variable types, and view the data using interactive plotting options.
convert single-member matrices to an ensemble table for import into the app.