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Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction.

Dongyang Li^1,2, Jianyi Yang¹, Yong Liu³

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310013, China.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This study introduces a novel demodulation method based on time-frequency analysis and principal component analysis (DPCA) for analyzing traction machine vibration signals. The DPCA method enhances the accuracy of rotating machinery state recognition, outperforming traditional FFT and STFT techniques.

Keywords:

feature extraction state identification traction machine vibration signal

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Area of Science:

Mechanical Engineering
Signal Processing
Condition Monitoring

Background:

Vibration signal analysis is crucial for rotating machinery state recognition.
Feature extraction from vibration signals is a critical step in this process.
Traction machines in elevators are essential rotating machinery components.

Purpose of the Study:

To analyze the time-frequency characteristics of traction machine vibrations under various operating conditions (direction, speed, load).
To develop and evaluate a novel demodulation method for improved feature extraction.
To compare the effectiveness of the new method against traditional techniques like FFT and STFT.

Main Methods:

Time-frequency analysis of vibration signals.
Development and application of a novel demodulation method combining time-frequency analysis and principal component analysis (DPCA).
Comparative analysis using Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT).

Main Results:

Traditional time-frequency methods (FFT, STFT) showed limited ability to differentiate vibration signals under varying load conditions.
The novel DPCA demodulation method effectively extracted periodic modulated wave signals.
DPCA demonstrated superior reliability and accuracy in state identification compared to FFT and STFT.

Conclusions:

The DPCA method offers a significant advancement in traction machine state identification.
This technique provides a reliable approach for analyzing complex vibration signals in rotating machinery.
DPCA enhances the accuracy of condition monitoring for traction systems.