Browse > Article
http://dx.doi.org/10.6109/jkiice.2022.26.7.981

Sound PSD Image based Tool Condition Monitoring using CNN in Machining Process  

Lee, Kyeong-Min (Department of Computer Engineering, Silla University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.7, 2022 , pp. 981-988 More about this Journal
Abstract
The intelligent production plant called smart factories that apply information and communication technology (ICT) are collecting data in real time through various sensors. Recently, researches that effectively applying to these collected data have gained a lot of attention. This paper proposes a method for the tool condition monitoring based on the sound signal generated in machining process. First, it not only detects a fault tool, but also presents various tool states according to idle and active operation. The second, it's to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. Finally, the contrast-enhanced PSD image obtained is diagnosed by using CNN. The results of the experiments demonstrate the high discrimination potential afforded by the proposed sound PSD image + CNN and show high diagnostic results according to the tool status.
Keywords
Tool Condition Monitoring; PSD(Power Spectral Density); CNN(Convolution Neural Network); Machining Process; Sound Signal;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 P. K. Kankar, S. C. Sharma, and S. P. Harsha, "Fault diagnosis of ball bearings using machine learning methods," Expert Systems with Applications, vol. 38, no. 3, pp. 1876-1886, Mar. 2011.   DOI
2 X. Yang, J. S. Lee, and H. K. Jung, "Fault Diagnosis Management Model using Machine Learning," Journal of Information and Communication Convergence Engineering, vol. 17, no. 2, pp. 128-134, Jun. 2019.   DOI
3 D. H. Kim, J. M. Lee, and J. D. Kim, "Design and Implementation of a Real-Time Product Defect Detection System based on Artificial Intelligence in the Press Process," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no 9, pp. 1144-1151, Sep. 2021.   DOI
4 S. Guo, T. Yang, W. Gao, C. Zhang, and Y. Zhang, "An Intelligent Fault Diagnosis Method for Bearings with Variable Rotating Speed based on Pythagorean Spatial Pyramid Pooling CNN," Sensors, vol. 18, no. 11, p. 3857, Nov. 2018.   DOI
5 H. D. Jeong, S. H. Kim, S. H. Woo, S. H. Kim, and S. C. Lee, "Real-Time Monitoring System for Rotating Machinery with IoT-based Cloud Platform," Transactions of the Korean Society of Mechanical Engineers, vol. 41, no. 6, pp. 517-524, Jun. 2017.   DOI
6 K, Patra, A. K. Jha, T. Szalay, J. Ranjan, and L. Monostori, "Artificial Neural Network Based Tool Condition Monitoring in Micro Mechanical Peck Drilling Using Thrust Force Signals," Precision Engineering, vol. 48, pp. 279-291, Apr. 2017.   DOI
7 A. V. Atli, O. Urhan, S. Erturk, and M. Sonmez, "A computer vision-based fast approach to drilling tool condition monitoring," in Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, London, U.K, vol. 220, no. 9, pp. 1409-1415, Sep. 2006.
8 K. -M. Lee, C. Vununu, K. -S. Moon, S. -H. Lee, and K. -R. Kwon, "Automatic Machine Fault Diagnosis System using Discrete Wavelet Transform and Machine Learning," Journal of Korea Multimedia Society, vol. 20, no. 8, pp. 1299-1311, Aug. 2017.   DOI
9 M. Saimurugan and K. I. Ramachandran, "A comparative study of sounds and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis," International Journal of Data Analysis Techniques and Strategies, vol. 6, no. 2, pp. 188-204, Jun. 2014.   DOI
10 Y. S. Wang, Q. H. Ma, Q. Zhu, X. T. Liu, and L. H. Zhao, "An intelligent approach for engine fault diagnosis based on Hilbert-Huang transform and support vector machine," Applied Acoustics, vol. 75, pp. 1-9, Jan. 2014.   DOI
11 Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May. 2015.   DOI
12 D. Verstraete, A. Ferrada, E. L. Droguett, V. Meruane, and M. Modarres, "Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings," Shock and Vibration, vol. 2017, pp. 1-17, Oct. 2017.
13 M. Zhao, M. Kang, B. Tang, and M. Pecht, "Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes," IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4290-4300, May. 2018.   DOI
14 C. Lu, Z. Wang, and B. Zhou, "Intelligent Fault Diagnosis of Rolling Bearing Using Hierarchical Convolutional Network based Health State Classification," Advanced Engineering Informatics, vol. 32, pp. 139-151, Apr. 2017.   DOI
15 F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data," Mechanical Systems and Signal Processing, vol. 72-73, pp. 303-315, May. 2016.   DOI
16 B. H. Park, Y. J. Lee, and C. W. Lee, "Tool Condition Monitoring Using Deep Learning in Machining Process," Journal of the Korean Society for Precision Engineering, vol. 37, no. 6, pp. 415-420, Jun. 2020.   DOI
17 W. Sun, R. Zhao, R. Yan, S. Shao, and X. Chen, "Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis," IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1350-1359, Jun. 2017.   DOI