Extracting weak multi-frequency signal in heavy colored noise
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(2020) 42:587
TECHNICAL PAPER
Extracting weak multi‑frequency signal in heavy colored noise Chen Yang1,2 · Jianhua Yang1,2 · Shuai Zhang1,2 · Houguang Liu1,2 Received: 24 December 2019 / Accepted: 5 October 2020 © The Brazilian Society of Mechanical Sciences and Engineering 2020
Abstract The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing. Keywords Colored noise · Weak multi-frequency signal · Normalized autocorrelation analysis · Bolt looseness fault · Compound fault
1 Introduction Signal analysis technology has important applications in many fields, such as biomedicine, speech recognition, fault diagnosis [1, 2]. Multi-frequency signal often contains more complex and comprehensive feature information, which is usually considered as a powerful representation of equipment state changes. The analysis of multi-frequency signal is helpful for maintenance personnel to handle equipment faults in time and reduce losses. However, under the influence of complex working conditions and transmission paths, there is a lot of background noise in the signal, which makes it difficult to extract the signal characteristics. Therefore, it Technical Editor: Thiago Ritto. * Jianhua Yang [email protected] 1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, People’s Republic of China
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou 221116, People’s Republic of China
2
is of great practical significance to detect weak multi-frequency signal from strong noise. In order to solve this problem, it is necessary to master the existing multi-frequency signal analysis methods. Stochastic resonance (SR) has the ability to enhance weak signals by adding noise with appropriate intensity to a system [3–5] an
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