A signal-filtering and feature-enhancement method based on ensemble local mean decomposition and adaptive morphological filtering

Author:

Zhou HaoORCID,Yang Jianzhong,Guo Gaofeng,Xiang HuaORCID,Chen Jihong

Abstract

Abstract The bearing fault signals from the spindle motors of computer numerical control machines are complex and non-linear due to being coupled to multiple subsystems. The complexity of industrial signals, with increased industrial noise, and the difference in fault features in different life cycles and different individual signals bring great challenges for fault feature extraction. In this paper, a signal-filtering and feature-enhancement method based on an ensemble local mean decomposition and adaptive morphological filtering (ELMD-AMF) method is proposed. First, the original vibration signal of the bearing is reconstructed by ELMD to reducing interference from background noise. Next, an improved feature-enhancement process based on AMF is constructed, a particle swarm optimization with maximum-weighted spectral kurtosis as an optimization objective is used to adaptively construct the size of the structural element, and a morphology hat product operator one is adapted to extract the periodic impulse features. Finally, the effectiveness of the method is proved by using an actual three-phase induction motor matched with an NTN ceramic bearing and a FAG metal bearing, respectively. Further, compared with minimum entropy deconvolution and fast kurtogram methods, the result proves that the proposed method has better performance for both early-failure and late-failure scenarios under real-world engineering conditions.

Funder

National High-Quality Development Project of China

Major Science and Technology Projects of Hubei Province

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes;Measurement Science and Technology;2024-07-09

2. Online early warning method for motorized-spindle degradation without failure data;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2024-06-18

3. An IGSA-VMD method for fault frequency identification of cylindrical roller bearing;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2024-05-21

4. A new approach to adaptive VMD based on SSA for rolling bearing fault feature extraction;Measurement Science and Technology;2023-12-08

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