Work

Research on tacholess fast-varying instantaneous rotating frequency estimation model based on nonlinear short-time Fourier transform combination with adaptive chirp mode decomposition and its application

Year: 2025

Type: article

Abstract: Instantaneous rotating frequency extraction is one of the key technologies for mechanical health monitoring and fault diagnosis. As the instantaneous rotating frequency presents fast-varying property under high-speed fluctuation, this paper uses a coarse-to-fine strategy to propose a tacholess fast-varying instantaneous rotating frequency estimation model based on nonlinear short-time Fourier transform (NLSTFT) combination with adaptive chirp mode decomposition (ACMD). By self-adaptive matching and decomposing the vibration signal based on its time-frequency distribution, it increases the energy aggregation of time-frequency representation, which not only improves computational efficiency but also avoids the spectral ambiguity problem. As a result, the proposed model is very suitable for extracting instantaneous rotating frequency under severe speed fluctuation; simulation signal and rolling bearing fault vibration signal also validate this conclusion. Furthermore, by integrating with signal decomposition technology, various order components of fault vibration signal can also be self-adaptive extracted.

Source: Journal of low frequency noise, vibration and active control

DOI: 10.1177/14613484251320211

Authors: Lu Yan, Tian Zhong Lan, Shi Li Yang, Qin Xiao Chen, Jin Wei Bie

Institution: Shanghai Maritime University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funder: National Natural Science Foundation of China