Robust Fractional Low Order Adaptive Linear Chirplet Transform and Its Application to Fault Analysis.
Junbo Long1, Changshou Deng1, Haibin Wang2
1College of Electronic Information Engineering, Jiujiang University, No. 551, Qianjin East Road, Jiujiang 332000, China.
Entropy (Basel, Switzerland)
|July 29, 2025
Summary
New fractional lower-order methods (FLOACT and FLOASCT) enhance time-frequency analysis for mechanical fault detection. These robust techniques improve signal clarity in noisy environments, aiding accurate fault identification.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Vibration Analysis
Background:
- Traditional time-frequency analysis (TFA) struggles with non-Gaussian mechanical fault signals due to complex noise.
- Infinite variance process noise and Gaussian colored noise degrade the time-frequency representation (TFR).
Purpose of the Study:
- To propose robust fractional lower-order adaptive linear chirplet transform (FLOACT) and fractional lower-order adaptive scaling chirplet transform (FLOASCT) methods.
- To suppress mixed complex noise for improved TFA of mechanical fault vibration signals.
Main Methods:
- Developing and detailing the calculation steps for FLOACT and FLOASCT algorithms.
- Utilizing the noise-insensitive properties of fractional lower-order statistics.
Main Results:
- Improved FLOACT and FLOASCT methods effectively analyze multi-component signals with short frequency intervals and cross-frequency trajectories.
- Demonstrated good performance even in strong impulse background noise environments.
Conclusions:
- The proposed FLOACT and FLOASCT methods show robustness and adaptability for mechanical outer race fault analysis in complex environments.
- Achieved good estimation accuracy and effectiveness at lower signal-to-noise ratios (MSNR).
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