A Novel Method for Enhanced Demodulation of Bearing Fault Signals Based on Acoustic Metamaterials
Authors:
- 19 January 2022
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Informatics
- Vol. 18 (10) , 6857-6864
Abstract
Rolling bearings fault diagnosis technology plays a significant role in modern industries. In this article, a novel method to diagnose bearing faults via acoustic metamaterials is proposed and experimentally validated by selecting and enhancing the optimal resonance frequency band for demodulation. The proposed method is performed by analysing acoustic signals, which is convenient due to its noncontact measurement. Different from conventional denoising techniques, the proposed method is implemented by enhancing the resonance frequency band via acoustic metamaterials instead of constructing filters to wipe out noise. Consequently, all the useful information of fault signals are remained and the detection limits of current acoustic sensing systems are improved because of the enhanced fault signals. Additionally, the optimal resonance band is sought by changing measured positions based on the frequency-selective enhancement property of the designed metamaterial. By comparing with spectral kurtosis-based method, the proposed method is more superior to extract bearing fault features at low signal to noise ratios. All the presented results indicate that the proposed method is effective to extract bearing fault features.Keywords
Funding Information
- National Natural Science Foundation of China (52175087)
This publication has 33 references indexed in Scilit:
- Enhanced sensing and conversion of ultrasonic Rayleigh waves by elastic metasurfacesScientific Reports, 2017
- Influence of Blade Pass Frequency Vibrations on MCSA-Based Rotor Fault Detection of Induction MotorsIEEE Transactions on Industry Applications, 2017
- Enhanced flexural wave sensing by adaptive gradient-index metamaterialsScientific Reports, 2016
- A Non-Invasive Method for Condition Monitoring of Induction Motors Operating Under Arbitrary Loading ConditionsArabian Journal for Science and Engineering, 2015
- Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault DiagnosisIEEE Transactions on Industrial Electronics, 2015
- Acoustic rainbow trapping by coiling up spaceScientific Reports, 2014
- Enhanced acoustic sensing through wave compression and pressure amplification in anisotropic metamaterialsNature Communications, 2014
- Acoustic rainbow trappingScientific Reports, 2013
- An enhanced Kurtogram method for fault diagnosis of rolling element bearingsMechanical Systems and Signal Processing, 2013
- A novel method for the optimal band selection for vibration signal demodulation and comparison with the KurtogramMechanical Systems and Signal Processing, 2011