  With the rapid development of smart manufacturing, data-driven machinery
health management has received a growing attention. As one of the most popular
methods in machinery health management, deep learning (DL) has achieved
remarkable successes. However, due to the issues of limited samples and poor
separability of different cavitation states of acoustic signals, which greatly
hinder the eventual performance of DL modes for cavitation intensity
recognition and cavitation detection. In this work, a novel multi-task learning
framework for simultaneous cavitation detection and cavitation intensity
recognition framework using 1-D double hierarchical residual networks (1-D
DHRN) is proposed for analyzing valves acoustic signals. Firstly, a data
augmentation method based on sliding window with fast Fourier transform
(Swin-FFT) is developed to alleviate the small-sample issue confronted in this
study. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is
constructed to capture sensitive features from the frequency domain acoustic
signals of valve. Then, a new structure of 1-D DHRN is proposed. Finally, the
devised 1-D DHRN is evaluated on two datasets of valve acoustic signals without
noise (Dataset 1 and Dataset 2) and one dataset of valve acoustic signals with
realistic surrounding noise (Dataset 3) provided by SAMSON AG (Frankfurt). Our
method has achieved state-of-the-art results. The prediction accurcies of 1-D
DHRN for cavitation intensitys recognition are as high as 93.75%, 94.31% and
100%, which indicates that 1-D DHRN outperforms other DL models and
conventional methods. At the same time, the testing accuracies of 1-D DHRN for
cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, 1-D
DHRN has also been tested for different frequencies of samples and shows
excellent results for frequency of samples that mobile phones can accommodate.
