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This paper presents a non-contact fault diagnostic method for ball bearingusing adaptive wavelet denoising, statistical-spectral acoustic features, andone-dimensional (1D) convolutional neural networks (CNN). The health conditionsof the ball bearing are monitored by microphone under noisy conditions. Toeliminate noise, adaptive wavelet denoising method based on kurtosis-entropy(KE) index is proposed. Multiple acoustic features are extracted base on expertknowledge. The 1D ResNet is used to classify the health conditions of thebearings. Case study is presented to examine the proposed method's capabilityto monitor the condition of ball bearing. The fault diagnosis results werecompared with and without the adaptive wavelet denoising. The results show itseffectiveness on the proposed fault diagnostic method using acoustic signals.

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