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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale

Xuezhuang E1, Wenbo Wang1

  • 1Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China.

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|August 12, 2023
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Summary

Loose escalator foundation bolts can cause serious safety issues. This study introduces a new method using empirical wavelet transform (EWT) and gray-gradient co-occurrence matrix (GGCM) to accurately detect bolt loosening degrees.

Keywords:
GGCMbispectrum analysisempirical wavelet decompositionescalatorfault identification

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Area of Science:

  • Mechanical Engineering
  • Condition Monitoring
  • Vibration Analysis

Background:

  • Escalator foundation bolt loosening is a critical safety concern, potentially leading to operational abnormalities and accidents.
  • Existing methods for detecting loose foundation bolts face challenges in fault feature extraction.

Purpose of the Study:

  • To develop an effective fault feature extraction method for detecting loosened escalator foundation bolts.
  • To improve the accuracy and reliability of escalator condition monitoring.

Main Methods:

  • Proposed a fault feature extraction method combining empirical wavelet transform (EWT) and gray-gradient co-occurrence matrix (GGCM).
  • Enhanced EWT's spectral partitioning using Teager energy operator and multi-scale peak determination to decompose vibration signals into empirical mode functions (EMFs).
  • Constructed GGCM for each EMF, calculated texture features, fused them, and used BiLSTM for identifying the degree of bolt loosening.

Main Results:

  • The proposed EWT and GGCM-based method effectively extracts fault features from escalator foundation vibration signals.
  • The fused multi-scale feature vector combined with BiLSTM accurately diagnosed the loosening degree of foundation bolts.
  • Experimental results demonstrate the method's effectiveness and potential for engineering applications.

Conclusions:

  • The developed method provides a more effective approach for diagnosing escalator foundation bolt loosening.
  • This technique offers significant engineering application value for enhancing escalator safety and maintenance.