Abnormal sound detection of washing machines based on convolution neural network in production environment

Abstract

Convolutional Neural Networks (CNN) have been shown to have great advantages equally in the field of image and audio. In the field of abnormal sounds detection of household appliances in the production environment, the fundamental difficulty is to extract and recognise the features that can represent abnormal sounds effectively. However, due to the lack of knowledge reserve and the wide variety of data volume, appropriate feature extraction is not easy in the actual production process of home appliance products. In this paper, an end-to-end CNN deep model framework is designed by us for washing machine type rotating machinery data analysis, which can perform adaptive mining on the features existing in the original rotating mechanical data even under the influence of different rotational speeds and considerable noise. By validating on real data sets with different characteristics, the results show that the method can realise online fast training learning and offline testing. The test time is shorter than one second, and the highest test classification accuracy is 99.3%.