Condition Monitoring of Refrigeration Compressor Using Sound Signal Based on Convolutional Neural Networks

被引：0
|
作者：
Kuendee, Punyisa [1 ]
机构：
[1] Siam Univ, Grad Sch Engn, Dept Engn Management, 38 Phet Kasem Rd, Bangkok, Thailand
来源：
3RD INTERNATIONAL CONFERENCE ON BIG DATA AND INTERNET OF THINGS (BDIOT 2019) | 2018年
关键词：
Refrigeration compressor; Streaming sound data; Internet of things; Machine learning; Convolutional neural networks; MACHINE;
D O I：
10.1145/3361758.3361772
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, the condition monitoring of refrigeration compressor used in ice making factory at Chonburi, Thailand is investigated. The streaming sound data collected from designed circuit with IoT technology is proposed. The 6-channel FFT spectrum training dataset with target dataset is introduced and used as input features for machine learning approaches such as ANN and CNN. In preliminary study, the target conditions are classified into 3 classes namely run open valve, run close valve and stop. The classification result demonstrates that the accuracy provided by ANN and CNN methods have similar values at approximately 99% in this study with comparison and evaluation.
引用
收藏
页码：105 / 109
页数：5
相关论文
共 50 条
  • [21] Person Identification by Footstep Sound Using Convolutional Neural Networks
    Algermissen, Stephan
    Hoernlein, Max
    APPLIED MECHANICS, 2021, 2 (02): : 257 - 273
  • [22] Lightweight Environmental Sound Recognition Using Convolutional Neural Networks
    Tang, Liwen
    Du, Zhouyang
    Wang, Yawen
    Xiao, Zhuoling
    Lin, Jiazhen
    Zhang, Xiaoyan
    2021 6TH INTERNATIONAL CONFERENCE ON UK-CHINA EMERGING TECHNOLOGIES (UCET 2021), 2021, : 215 - 220
  • [23] ROBUST SOUND EVENT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS
    Zhang, Haomin
    McLoughlin, Ian
    Song, Yan
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 559 - 563
  • [24] Study of Discharge Sound Diagnosis Using Convolutional Neural Networks
    Xu, Peng
    Sun, Hanwen
    Li, Zhe
    Zhou, Guliang
    Li, Jun
    Zhou, Lubo
    Tian, Haoyang
    Ji, Yiping
    2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020), 2020, : 1355 - 1359
  • [25] Driver Drowsiness Monitoring using Convolutional Neural Networks
    Victoria, D. Rosy Salomi
    Mary, D. Glory Ratna
    2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, AND INTELLIGENT SYSTEMS (ICCCIS), 2021, : 1055 - 1059
  • [26] Vehicle Speed Monitoring using Convolutional Neural Networks
    Barth, V.
    de Oliveira, R.
    de Oliveira, M.
    do Nascimento, V.
    IEEE LATIN AMERICA TRANSACTIONS, 2019, 17 (06) : 1000 - 1008
  • [27] Agricultural Vegetation Monitoring Based on Aerial Data Using Convolutional Neural Networks
    V. Ganchenko
    A. Doudkin
    Optical Memory and Neural Networks, 2019, 28 : 129 - 134
  • [28] Agricultural Vegetation Monitoring Based on Aerial Data Using Convolutional Neural Networks
    Ganchenko, V
    Doudkin, A.
    OPTICAL MEMORY AND NEURAL NETWORKS, 2019, 28 (02) : 129 - 134
  • [29] Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks
    Jia, Xiongjie
    Han, Yang
    Li, Yanjun
    Sang, Yichen
    Zhang, Guolei
    ENERGY REPORTS, 2021, 7 : 6354 - 6365
  • [30] Tool condition monitoring (TCM) using neural networks
    Liu, Tien-I
    Jolley, Bob
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 78 (9-12): : 1999 - 2007
12345