  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.
