4.7 Article

An active noise control method of non-stationary noise under time-variant secondary path

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 149, Issue -, Pages -

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2020.107193

Keywords

Active noise control; Non-stationary noise; Signal decomposition; Simultaneous perturbation

Funding

  1. National Natural Science Foundation of China [11304019, 61674017, 11974376]

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This paper proposes a novel active noise control method based on signal decomposition and simultaneous perturbation, which effectively controls non-stationary reference noise under a time-variant secondary path by decomposing the reference noise into a series of intrinsic mode functions and applying simultaneous perturbation for control. Experiments demonstrate that the proposed method outperforms commonly used algorithms.
Active noise control (ANC) provides an efficient way for eliminating low-frequency noise, whereas accurate controlling non-stationary noise of rotating machinery under time -variant secondary path is still a major challenge. To address this, a novel ANC method based on signal decomposition and simultaneous perturbation is developed in this paper. In the proposed method, an indicator of periodic characteristic (IPC) with the degree of cyclostationarity is first presented to evaluate reference noise. Subsequently, a real-time strategy of the IPC combined with fast empirical mode decomposition is designed to adaptively decompose reference noise into a series of intrinsic mode functions, which are stationary and further controlled individually. Besides, the simultaneous perturbation is applied to update the controller weights for having a better tracking ability under the time-variant secondary path. Experiments were conducted to evaluate the capacity of the proposed method for controlling non-stationary reference noise under the time -variant secondary path. The results demonstrate that proposed method is capable of performing better than commonly used algorithms. (c) 2020 Elsevier Ltd. All rights reserved.

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