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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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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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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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Related Experiment Video

Updated: Dec 6, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Baseline-Free Adaptive Crack Localization for Operating Stepped Rotors Based on Multiscale Data Fusion.

Zhiwen Lu1,2, Shancheng Cao3, Rui Yuan1,2

  • 1Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.

Sensors (Basel, Switzerland)
|October 10, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for accurately locating cracks in running rotors, even with steps and noise. It uses fused data from multiscale super-harmonic characteristic deflection shapes (SCDSs) for improved machinery diagnostics.

Keywords:
Crack localizationD-S evidence fusionmultiscalenonlinearrotorssuper-harmonic characteristic deflection shape

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Area of Science:

  • Mechanical Engineering
  • Structural Health Monitoring
  • Vibration Analysis

Background:

  • Crack localization in running rotors is crucial for machinery operation and maintenance.
  • Existing characteristic deflection shape methods face challenges with steps and noise without baseline data.
  • Accurate crack detection is vital for preventing catastrophic failures and ensuring system reliability.

Purpose of the Study:

  • To develop a baseline-free adaptive crack localization method for running rotors.
  • To address challenges of interference from rotor steps and improve performance in noisy environments.
  • To enhance the efficiency and practical applicability of crack localization techniques.

Main Methods:

  • Proposed a novel baseline-free adaptive crack localization method using data fusion of multiscale super-harmonic characteristic deflection shapes (SCDSs).
  • Utilized crack-induced asymmetry and crack breathing nonlinearity to eliminate step interference without a reference model.
  • Employed Gaussian multiscale space and the Teager energy operator to create multiscale Teager super-harmonic characteristic deflection shapes (TSCDSs) for noise robustness.
  • Used fractal dimension for adaptive selection of TSCDSs and Dempster-Shafer's (D-S) evidence fusion to derive a new damage index for localization.

Main Results:

  • Successfully localized single or multiple cracks accurately, overcoming interference from rotor steps.
  • Demonstrated improved crack localization performance in noisy environments.
  • Verified the feasibility and effectiveness of the proposed method through numerical simulations and experimental investigations.

Conclusions:

  • The developed baseline-free adaptive method effectively localizes cracks in running rotors.
  • The data fusion of multiscale TSCDSs offers enhanced robustness against noise and interference.
  • This approach provides a promising solution for practical machinery diagnostics and maintenance.