Article: Abnormal sound detection of washing machines based on convolution neural network in production environment Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2021 Vol.20 No.3 pp.201 - 211 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%. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Jingkai Ma; Nan Li; Yong Jiang; Tao Feng

Addresses: College of Artificial Intelligence, Beijing Technology and Business University, Beijing, China ' College of Artificial Intelligence, Beijing Technology and Business University, Beijing, China ' College of Artificial Intelligence, Beijing Technology and Business University, Beijing, China ' College of Artificial Intelligence, Beijing Technology and Business University, Beijing, China

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%.

Keywords: household appliances; convolutional neural networks; deep neural networks; audio feature; abnormal sound detection.

DOI: 10.1504/IJWMC.2021.115660

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.3, pp.201 - 211

Received: 28 Sep 2020
Accepted: 16 Dec 2020
Published online: 15 Jun 2021 *

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

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