Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet Denoising

10/07/2021

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This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy conditions. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed method's capability to monitor the condition of ball bearing. The fault diagnosis results were compared with and without the adaptive wavelet denoising. The results show its effectiveness on the proposed fault diagnostic method using acoustic signals.

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Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet Denoising

A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis

Denosing Using Wavelets and Projections onto the L1-Ball

Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM)

Temporal clustering network for self-diagnosing faults from vibration measurements

Quick Learning Mechanism with Cross-Domain Adaptation for Intelligent Fault Diagnosis

SREL: Severity Rating Ensemble Learning for Non-Destructive Fault Diagnosis of Cu Interconnects using S-parameter Patterns

Towards zero-configuration condition monitoring based on dictionary learning

Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet Denoising

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