Research on sound quality of roller chain transmission system based on multi-source transfer learning

• Published in: Scientific reports 2024-05, Vol.14 (1), p.11226-11226, Article 11226
• Main Authors: Li, Jiabao, An, Lichi, Cheng, Yabing, Wang, Haoxiang
• Format: Article
• Language: English
• Subjects: 639/166; 639/166/988; Accuracy; Deep learning; Humanities and Social Sciences; Machine learning; Methods; multidisciplinary; Muti-source domain; Neural networks; Roller chain; Science; Science (multidisciplinary); Signal processing; Sound; Sound quality; Transfer learning; Working conditions
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-62090-3

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.