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Advanced method for recognizing and measuring key information in non-stationary signals using variational mode

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    Area of Science:

    • Signal Processing
    • Machine Learning
    • Industrial Monitoring

    Background:

    • Non-stationary signals in industrial settings (logistics, fault diagnosis) pose challenges for accurate identification and measurement.
    • Existing methods struggle with the complexity and variability of these signals.

    Purpose of the Study:

    • To develop a robust method for identifying and measuring key information from non-stationary industrial signals.
    • To improve the accuracy and reliability of signal analysis in critical industrial applications.

    Main Methods:

    • Utilized variational mode decomposition (VMD) for signal reconstruction.
    • Constructed feature matrices using upper envelope, moving kurtosis, and moving root mean square.
    • Employed a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Support Vector Machine (SVM) model (CNN-LSTM-SVM) for feature identification and measurement.

    Main Results:

    • The proposed CNN-LSTM-SVM method achieved high recognition accuracies of 99.17% on synthetic signals.
    • The method demonstrated excellent performance on real-world collected signals, achieving 99.02% accuracy.

    Conclusions:

    • The integrated VMD and CNN-LSTM-SVM approach effectively addresses the limitations in identifying key information from non-stationary industrial signals.
    • This method offers a significant advancement for state detection and fault diagnosis in industrial environments.