Data Augmented of Mechanical Fault Sound Signal based on Generative Adversarial Networks

Yining Yang, Xiang Su, Nan Li
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Abstract

In this paper, a global average pooling convolutional neural network based on CNN is proposed for mechanical fault sound detection, which called as GCMD. To solve the data scarcity of mechanical fault sound data, a spectrum frame selection augmented method based on log Mel spectrum feature is proposed to augment the original data, that aim is to train GCMD and generate counter networks. In order to solve the unbalance problem of data set and further improve the generalization ability of GCMD, an augmented neural network model based on CapsuleGAN was proposed, which called MFS-CapsuleGAN. The model was evaluated on the augmented data set by training GCMD neural network. Compared with the original data set, the accurate recognition rate of the model was improved by 23.7%. The performance of this method is improved significantly, which proves the feasibility and effectiveness of MFS-CapsuleGAN data augmented. In addition, the data set with background noise was used to test the generalization ability of GCMD network. The fluctuation range was within 0.117, indicating the good robustness of GCMD network.
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基于生成对抗网络的机械故障声信号数据扩充
本文提出了一种基于CNN的全局平均池化卷积神经网络用于机械故障声音检测,称为GCMD。为了解决机械故障声音数据的数据稀缺性,提出了一种基于对数Mel谱特征的频谱帧选择增强方法,对原始数据进行增强,目的是训练GCMD并生成对抗网络。为了解决数据集不平衡问题,进一步提高GCMD的泛化能力,提出了一种基于CapsuleGAN的增强神经网络模型,称为MFS-CapsuleGAN。通过训练GCMD神经网络在增强数据集上对模型进行评价。与原始数据集相比,模型的准确识别率提高了23.7%。该方法的性能得到了显著提高,证明了MFS-CapsuleGAN数据增强的可行性和有效性。此外,利用背景噪声数据集对GCMD网络的泛化能力进行了测试。波动范围在0.117以内,说明GCMD网络具有较好的鲁棒性。
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来源期刊
Periodica polytechnica Electrical engineering and computer science
Periodica polytechnica Electrical engineering and computer science Engineering-Electrical and Electronic Engineering
CiteScore
2.60
自引率
0.00%
发文量
36
期刊介绍: The main scope of the journal is to publish original research articles in the wide field of electrical engineering and informatics fitting into one of the following five Sections of the Journal: (i) Communication systems, networks and technology, (ii) Computer science and information theory, (iii) Control, signal processing and signal analysis, medical applications, (iv) Components, Microelectronics and Material Sciences, (v) Power engineering and mechatronics, (vi) Mobile Software, Internet of Things and Wearable Devices, (vii) Solid-state lighting and (viii) Vehicular Technology (land, airborne, and maritime mobile services; automotive, radar systems; antennas and radio wave propagation).
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