Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 108-117.doi: 10.6040/j.issn.1672-3961.0.2019.419

• Machine Learning & Data Mining • Previous Articles     Next Articles

Abnormal sound detection of washing machines based on deep learning

Chunyang LI(),Nan LI*(),Tao FENG,Zhuhe WANG,Jingkai MA   

  1. School of Material and Mechanical Engineering, Beijing Technology and Business University, Beijing 100048, China
  • Received:2019-07-22 Online:2020-04-20 Published:2020-04-16
  • Contact: Nan LI E-mail:chuny6896@163.com;linan@th.btbu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61877002);国家自然科学基金资助项目(51405005)

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Abstract:

Based on the convolutional neural network (CNN) framework, a model for abnormal sounds recognition of washing machine was proposed. According to the remarkable feature extraction ability and translation invariance of convolutional neural network, the abnormal sound features of washing machines were learned, so as to achieve the purpose of the automatic intelligent recognition of abnormal sounds for washing machines in production line. This method provided a complete process to solve the problems of training datasets establishment and data imbalance. A network model for data augmentation called advanced deep convolution generated adversarial network (ADCGAN)was proposed to solve the problem of training data scarcity. The traditional deep convolution generated adversarial network (DCGAN) model was improved to better adapt to the generation of industrial sounds. This model could be used to extend the original data and generate the abnormal sound augmented datasets of washing machine. The augmented datasets was used to train the convolutional neural network, and the test accuracy reached 0.999. The generalization ability of abnormal sounds recognition model for washing machine network was tested by using the data set with background noise signal added. The correct recognition rate reached 0.902, which indicated that this network had good robustness in recognizing abnormal noises of washing machines.

Key words: audio classification, convolution neural network, generative adversarial networks, deep learning

CLC Number: 

  • TP181

Fig.1

Sound collectionof washing machine on the production line"

Fig.2

Production of original sound data set"

Fig.3

Training of convolutional neural network by enhanced dataset"

Fig.4

Network diagram of ADCGAN"

Fig.5

Structure diagram of abnormal sound detection model of washing machine based on CNN"

Fig.6

The variation of Vloss with training times"

Fig.7

The variation of Dloss and Glosswith training times"

Table 1

Networktraining parameters of washing machine abnormalsound recognition"

学习速率η Dropout概率系数p 平滑参数 批量大小 迭代次数
0.000 1 0.6 0.25 200 20 000

Fig.8

Classification accuracy of abnormal sound recognition network of washing machine trained with original data set"

Fig.9

Classification accuracy of abnormal sound recognition network of washing machine trained with augmented data set"

Fig.10

Test accuracy of CNN model trained by datasets of different sampling frequencies"

Table 2

Cross-validation Accuracy of Each Models"

模型 测试精度
CNN 0.999
RNN 0.687
DNN 0.992
SVM 0.681
LSTM 0.995
MLP 0.994

Fig.11

Comparison of waveforms of normal sound data with and without noise"

Fig.12

Comparison of waveforms with and without noise in abnormal sound data"

Table 3

Cross-validationaccuracy for each dataset of CNN"

数据集 测试精度
原始数据集 0.923
加噪数据集(SNR=10 dB) 0.902
增强训练集 0.999
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