Development of Anomaly Detection Technology Applicable to Various Equipment Groups in Smart Factory

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 27, 2024
KIWON PARK Myoung Gyo Lee Sung Yong Cho Yoon Jang Young Tae Choi

Abstract

This study delves into the creation of anomaly detection technology applicable to a range of equipment groups within smart factories. This advanced technology uses high-performance MEMS vibration sensors, edge CMS devices, and PHM platforms to tackle issues such as data imbalance, learning model limitations, complex equipment operating patterns, and real-time processing. It also addresses central server concentration, data cycling problem, various equipment classification, and algorithm operation problems that can arise when implementing systems in the field. Using AI-based vibration detection algorithms, data can be collected at high sampling rates and analyzed in real-time through edge computing, minimizing latency and mitigating server capacity issues compared to cloud-based analytics. The system continually monitors and learns standard performance data from equipment to provide practical solutions that minimize equipment failures and downtimes. The results of this study are impressive, as it has successfully developed anomaly detection framework and PHM systems that are expected to enhance the efficiency and sustainability of smart factories. Furthermore, the study aims to showcase and improve the effectiveness of predictive maintenance in both domestic and international automotive factory production lines. This revolutionary technology will be a key component in smart and software-defined factories and help companies achieve intelligent automation.

How to Cite

PARK, K., Lee, M. G. ., Cho, S. Y. ., Jang, Y. ., & Choi, Y. T. . (2024). Development of Anomaly Detection Technology Applicable to Various Equipment Groups in Smart Factory. PHM Society European Conference, 8(1), 12. https://doi.org/10.36001/phme.2024.v8i1.4037
Abstract 305 | PDF Downloads 214

##plugins.themes.bootstrap3.article.details##

Keywords

Anomaly detection framework, PHM system, PHM Platform, Condition Monitoring System, Edge computing device, Cycling techniques, FFT, STFT, Auto-encoder, Smart Factory, Industrial Robot, Motor, Reducer, Anomaly score

References
[1] Niklas Tritschler, Andrew Dugenske, Thomas Kurfess. (2021). An Automated Edge Computing-Based Condition Health Monitoring System: With an Application on Rolling Element Bearings. Journal of Manufacturing Science and Engineering. 143(7): 071006 (8ps)

[2] JK Chow, Z Su, J Wu, PS Tan, X Mao. (2020). Anomaly detection of defects on concrete structures with the convolutional autoencoder. Advanced Engineering Informatics. Elsevier, Volume 45, 101105.

[3] Corbinian Nentwich and Gunther Reinhart. (2021). A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears. Robotics 2021, 10(2), 80.

[4] M Pech, J Vrchota, J Bednář. (2021). Predictive maintenance and intelligent sensors in smart factory. Sensors, 2021, 21(4), 1470.

[5] Tareq Tayeh, Abdallah Shami. (2021). Anomaly Detection in Smart Manufacturing with an Application Focus on Robotic Finishing Systems: A Review. arXiv:2107.05053 (cs.RO).

[6] A. Bonci, S. Longhi, G. Nabissi and F. Verdini. (2019). Predictive Maintenance System using motor current signal analysis for Industrial Robot. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, pp. 1453-1456.

[7] S. K. Bose, B. Kar, M. Roy, P. K. Gopalakrishnan, and A. Basu. (2019). Adepos: Anomaly detection based power saving for predictive maintenance using edge computing. Proceedings of the 24th Asia and South Pacific Design Automation Conference, pp. 597–602.

[8] Pál Péter Hanzelik, Alex Kummer, János Abonyi. (2022). Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development. Sensors 2022, 22(11), 4268.

[9] Ke Feng, J.C. Ji, Qing Ni, Michael Beer. (2023). A review of vibration-based gear wear monitoring and prediction techniques. Mechanical Systems and Signal Processing Volume 182, 109605.

[10] S. S. Patil and J. A. Gaikwad. (2013). Vibration analysis of electrical rotating machines using FFT: A method of predictive maintenance. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, pp. 1-6.

[11] George M. Alber and Alan G. Marshall. (1990). Effect of Sampling Rate on Fourier Transform Spectra: Oversampling is Overrated. Appl. Spectrosc. 44, 1111-1116.

[12] Vanraj, Deepam Goyal, Abhineet Saini, S. S. Dhami, B. S. Pabla. (2016). Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniques — A review. 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring). Dehradun, India, pp. 1-6.

[13] T. Chen, X. Liu, B. Xia, W. Wang and Y. Lai. (2020). Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder. IEEE Access, vol. 8, pp. 47072-47081.

[14] Dan Li, Guoqiang Hu, Costas J. Spanos. (2016). A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis. Energy and Buildings Volume 128, Pages 519-529.

[15] Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao. (2019). An Empirical Study on Generalizations of the ReLU Activation Function. ACM SE '19: Proceedings of the 2019 ACM Southeast Conference Pages 164–167.

[16] Yasi Wang, Hongxun Yao, Sicheng Zhao. (2016). Auto-encoder based dimensionality reduction. Neurocomputing, Volume 184, Pages 232-242.

[17] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818-2826.

[18] M. Chen, X. Shi, Y. Zhang, D. Wu and M. Guizani. (2021). Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network. IEEE Transactions on Big Data, vol. 7, no. 4, pp. 750-758.

[19] Unai Izagirre, Imanol Andonegui, Itziar Landa-Torres, Urko Zurutuza. (2022). A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines. Robotics and Computer-Integrated Manufacturing Volume 74, 102287.

[20] Athina Tsanousa, Evangelos Bektsis, Constantine Kyriakopoulos, Ana Gómez González, Urko Leturiondo, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris. (2022). A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends. Sensors 2022, 22(5), 1734.

[21] H. Yan, J. Wan, C. Zhang, S. Tang, Q. Hua and Z. Wang. (2018). Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning. IEEE Access, vol. 6, pp. 17190-17197.
Section
Technical Papers