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http://dx.doi.org/10.12812/ksms.2021.23.4.093

Comparative Analysis of Machine Learning Algorithms for Healthy Management of Collaborative Robots  

Kim, Jae-Eun (Graduate School of Business Administration, University of Ulsan)
Jang, Gil-Sang (Deptment of Management Information Systems, University of Ulsan)
Lim, KuK-Hwa (Graduate School of Business Administration, University of Ulsan)
Publication Information
Journal of the Korea Safety Management & Science / v.23, no.4, 2021 , pp. 93-104 More about this Journal
Abstract
In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.
Keywords
Cooperative robot; Robot data; Machine learning; Artificial neural network; Overload prediction; Work load prediction;
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1 S. T. Han et al.(2011), "A study on the judgement rating for level of need for long-term care insurance using a decision tree." CSAM (Communications for Statistical Applications and Methods), 18(1):137-146.
2 L. Breiman(2001), "Random forests." Machine Learning, 45(1):5-32.   DOI
3 J. Park, M. Chae, S. Jung(2016), "Classification model of types of crime based on random-forest algorithms and monitoring interface design factors for real-time crime prediction." KIISE Transactions on Computing Practices, 22(9):455-460. https://doi.org/10.5626/KTCP.2016.22.9.455   DOI
4 G. Xu et al.(2019), "Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning." Sensors, 19(5):1088. https://doi.org/10.3390/s19051088   DOI
5 E. Raczko, B. Zagajewski(2017), "Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images." European Journal of Remote Sensing, 50(1):144-154. https://doi.org/10.1080/22797254.2017.1299557   DOI
6 S. M. Chun, S. Y. Suk(2019), "Development and implementation of smart manufacturing big-data platform using opensource for failure prognostics and diagnosis technology of industrial robot." IEMEK Journal of Embedded Systems and Applications, 14(4):187-195. https://doi.org/10.14372/IEMEK.2019.14.4.187   DOI
7 J. H. Jang et al.(2011), "Propose the method for monitoring of LCD conveyance robot condition using vibration signal." Proceedings of the Korean Society for Noise and Vibration Engineering Conference, Korea, 584-585.
8 R. N. A. Algburi, H. Gao(2019), "Health assessment and fault detection system for an industrial robot using the rotary encoder signal." Energies, 12(14): 2816. https://doi.org/10.3390/en12142816   DOI
9 A. C. Bittencourt et al.(2011), "Modeling and identification of wear in a robot joint under temperature uncertainties." IFAC Proceedings, 44(1):10293-10299. https://doi.org/10.3182/20110828-6-IT-1002.01078   DOI
10 T. Borgi et al.(2017), "Data analytics for predictive maintenance of industrial robots." 2017 International Conference on Advanced Systems and Electric Technologies(IC_ASET), Hammamet, Tunisia. https://doi.org/10.1109/ASET.2017.7983729   DOI
11 M. Zhao et al.(2018), "Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox." Mechanical Systems and Signal Processing, 98:16-31. https://doi.org/10.1016/j.ymssp.2017.04.033   DOI
12 Z. Guo, M. Liu, Z. Xiong(2019), "Fault diagnosis of motor based on VMD-sample entropy-random forest." Journal of Physics: Conference Series, 1345(3). https://doi.org/10.1088/1742-6596/1345/3/032099   DOI
13 S. P. Curram, J. Mingers(1994), "Neural networks, decision tree induction and discriminant analysis: An empirical comparison." Journal of the Operational Research Society, 45(4):440-450. https://doi.org/10.1057/jors.1994.62   DOI
14 C. Krauss, X. A. Do, N. Huck(2017), "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500." European Journal of Operational Research, 259(2):689-702. https://doi.org/10.1016/j.ejor.2016.10.031   DOI
15 D. W. Ruck, S. K. Rogers, M. Kabrisky(1990), "Feature selection using a multilayer perceptron." Journal of Neural Network Computing, 2(2):40-48.
16 S. Bae, J. Yu(2018), "Estimation of the apartment housing price using the machine learning methods: The case of Gangnam-gu, Seoul." Journal of the Korea Real Estate Analysts Association, 24(1):69-85. https://doi.org/10.19172/KREAA.24.1.5   DOI
17 Y. H. Lee et al.(2019), "Seq2Seq model-based prognostics and health management of robot arm." The Journal of Korea Institute of Information, Electronics, and Communication Technology, 12(3):242-250. https://doi.org/10.17661/jkiiect.2019.12.3.242   DOI
18 C. Deb et al.(2016), "Fault diagnosis of a single point cutting tool using statistical features by random forest classifier." Indian Journal of Science and Technology, 9(33):1-8. https://doi.org/10.17485/ijst/2016/v9i33/101340   DOI
19 Y. Liu et al.(2020), "Fault diagnosis of power transformer based on tree ensemble model." IOP Conference Series: Materials Science and Engineering, 715(1). https://doi.org/10.1088/1757-899X/715/1/012032   DOI
20 P. Cao, S. Zhang, J. Tang(2018), "Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning." IEEE Access, (6):26241-26253. https://doi.org/10.1109/ACCESS.2018.2837621   DOI
21 S. Nawar, A. M. Mouazen(2017), "Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon." Sensors, 17(10):2428. https://doi.org/10.3390/s17102428   DOI
22 I. Nitze, U. Schulthess, H. Asche(2012), "Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification." Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, p. 035.
23 K. Bhakta et al.(2019), "Fault diagnosis of induction motor bearing using cepstrum-based preprocessing and ensemble learning algorithm." 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar, Bangladesh. https://doi.org/ 10.1109/ECACE.2019.8679223   DOI
24 A. A. Jaber, R. Bicker(2016), "Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network." Insight-Non-Destructive Testing and Condition Monitoring, 58(4):179-186. https://doi.org/10.1784/insi.2016. 58.4.179   DOI
25 J. Lee et al.(2014), "Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications." Mechanical Systems and Signal Processing, 42(1-2):314-334. https://doi.org/10.1016/j.ymssp.2013.06.004   DOI
26 H. Yoon(2019), "DBSCAN parameter optimization of predictive maintenance system for wafer transfer robot using design of experiment." Master's thesis, Myongji University.