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coremicro® Real-Time Structure Health Monitoring and Vibration Analysis Kernel
RTSHM-kernel



RTSHM-Kernel Main Screen

Two main functionalities are enabled by the coremicro® RTSHM-kernel product:

  • To provide the user with tools for analyzing vibration signals in data logs
  • To enable the extraction of features that are correlated with structural health (preprocessing framework)

This is a general software tool that allows characterization of events associated with a structure's state. Features highly correlated to structure conditions can be identified and used for further processing by custom algorithms (user application software). In particular an advantage of this product is that it can be combined with Optimized Neuro Genetic Fast Estimator (ONGFE), an ANN based software toolset, for conducting automated diagnostics. In this case, data logs which contain vibration signals that correspond to representative structure states can be used (after applying the selected RTSHM-kernel's feature extraction algorithms for training the ONGFE's diagnostic neural networks) to perform real-time on-line diagnostics). The main screen of the RTSHM-kernel (as shown in the figure) provides the following main features:

  • Data log feature extraction plotting and recording. In this Graphical User Interface (GUI) section, the user has the ability to perform analysis of a vibration signal that has been captured in a data log. After entering the file name and selecting acquire data, the following functions are available:
    1) View the evolution of the time-domain signal where a vibration stream feeds the measurement window.
    2) Compute, plot, and record six features of the time-domain signal. Baseline features are the signal’s mean, rms, variance, kurtosis, crest factor, and peak amplitude for a certain window size (default window length is 512 samples)
    3) View the evolution of the frequency domain signal (via the DFT), where vibration streams are processed for updating the screen. Spectrum patterns can be analyzed off line for identifying and visualizing frequency data patterns.
    4) Compute, plot, and record six features of the frequency-domain signal. Baseline features are the signal’s mean, rms, variance, kurtosis, crest factor, and peak amplitude for a certain window size (default window length is 512 samples)

    5) Activate multi-resolution analysis (MRA) and plot/record results for four types of wavelets. Four wavelet algorithms are provided: (1) the standard discrete Haar transform (S-DHT); (2) lifting scheme discrete Haar transform (LS-DHT); (3) Linear interpolation wavelets; and (4) Daubechies D4 wavelet. Multi-resolution analysis is then made possible by allowing the user to select a desired MRA level for generating a set of averages and coefficients.


Time Domain Features

  • The second major section in this page is feature extraction for designing pattern recognition algorithms (such as neural network). In the case of methods based on Artificial Neural Networks (ANN), the user can identify, select, and extract parameters which are correlated with the system response (features) for building neural network training and testing files. This process entails the following:

    1) Compute and plot a characteristic frequency response for a representative state (such as normal operation) of a structural component.

    2) Extract as many as 11 major features from the representative data log as well as for up to three additional logs.

    3) Generate neural network training and testing files that are built using the extracted features as inputs and user-defined class IDs as outputs.


Feature Selection

Computational Requirements:
• Windows Vista, Windows XP


 
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