A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals. (August 2022) - Sha, Yu; Faber, Johannes; Gou, Shuiping; Liu, Bo; Li, Wei; Schramm, Stefan; Stoecker, Horst; Steckenreiter, Thomas; Vnucec, Domagoj; Wetzstein, Nadine; Widl, Andreas; Zhou, Kai
Abstract: 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. Also different tasks were performed separately conventionally. 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 accuracies 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. Highlights: A multi-task learning framework for simultaneous cavitation detection and cavitation intensity recognition is proposed. The 1-D double hierarchical residual blocks (1-D DHRB) with large kernel are proposed as a feature extractor on valve acoustic signals. The sliding window with Fast Fourier Transform (Swin-FFT) data augmentation method is proposed to tackle the small-sample problem. The proposed method is tested on three different real-world datasets of acoustic signals and compared with the state-of-the-art methods. The impact of different sampling rate of the acoustics signals on our proposed method for cavitation recognition is investigated.