基于域泛化的工业设备无监督异常声音检测算法.

In industrial scenarios, acoustic properties between training and test data would be changed due to the rarity and high diversity of equipment anomalies, as well as differences in machine operating conditions or ambient noise during the training and test phases. To solve these problems, an unsupervised anomalous sound detection algorithm based on joint deep learning and variable Bayesian Gaussian mixture model was proposed. The information was extracted through the joint training of two kinds of neural networks, and cluster analysis was carried out on the embedding obtained by variable Bayesian Gaussian mixture model. A new hybrid sample data enhancement method was introduced to generate samples with a combination of alternative methods to align the distribution between different domains. An improved sub-cluster AdaCos was applied to exclude potential outliers. The experimental results show that the average AUC (area under curve) of the target domain on the datasets of three industrial machine types reaches 79. 03%, and the average F1 score reaches 67. 23%. Compared with the baseline model, the average harmonic value is improved by about 20%, and it performs well in unsupervised anomalous sound detection of industrial equipment. [ABSTRACT FROM AUTHOR]