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High-dimensional optimized extraction chirplet transform: Algorithm and applications.

Long Ge¹, Dezun Zhao², Tianyang Wang³

¹Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.

ISA Transactions

|January 16, 2026

Summary

This summary is machine-generated.

The high-dimensional optimized extraction chirplet transform (HOECT) improves time-frequency analysis for complex signals. This method accurately identifies rotating machinery faults and analyzes whale sounds, even with noise.

Keywords:

Chirplet transform Connected domain analysis Mechanical fault diagnosis Smoothing extraction operator Time-frequency analysis Window parameter adaptive optimization criterion

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Area of Science:

Signal Processing
Mechanical Engineering
Acoustics

Background:

Rotating machinery under varying conditions generates complex vibration signals with dense, non-proportional frequency components.
Existing time-frequency analysis (TFA) techniques struggle to achieve high-precision and concentrated time-frequency representations (TFRs) for such signals.
Accurate analysis is crucial for operational state characterization and fault diagnosis in machinery.

Purpose of the Study:

To propose a novel high-dimensional optimized extraction chirplet transform (HOECT) for improved TFA.
To enhance the precision and energy concentration of TFRs for complex non-stationary signals.
To validate the HOECT's effectiveness in machinery fault diagnosis and analysis of other complex signals like whale sounds.

Main Methods:

Developed a window parameter adaptive optimization criterion to dynamically match signal characteristics.
Improved component matching chirplet transform (CMCT) using the adaptive criterion for frequency component separation in the time-frequency-chirprate domain.
Constructed a smoothing extraction operator (SEO) in the frequency-chirprate domain for enhanced energy concentration.
Introduced a connected domain analysis (CDA) algorithm to suppress noise and highlight signal features.

Main Results:

The HOECT accurately characterizes dense and non-proportional frequency components with high energy concentration.
Demonstrated superiority in operational state characterization and fault feature identification using planetary gearbox vibration signals.
Showcased strong robustness and applicability in analyzing complex non-stationary signals, verified with whale sound data.

Conclusions:

The proposed HOECT offers a significant advancement in time-frequency analysis for complex non-stationary signals.
HOECT provides a powerful tool for accurate fault diagnosis in rotating machinery.
The method's robustness extends its applicability to diverse fields, including bioacoustics.