IIoT Based Anomaly Detection and Maintenance Management of an Industrial Rotary System
Article Sidebar
Main Article Content
Abstract
Numerous challenges are being faced by implementing the Industrial Internet of Things (IIoT), which enables anomaly detection and optimal maintenance management of industrial production systems and their critical machines. Industries have started adopting this revolutionary technology along with other allied technologies to reap the full benefits out of Industry 4.0 environment. The objectives of this research work were to use the IIoT to develop a continuous monitoring system of the behavior of bottleneck facilities in a production system, to predict and avoid all possible failures, and to improve the overall productivity of the manufacturing system. The proposed prognostic health monitoring system employs IIoT sensors to measure the current values of the operating parameters of a machine, using built-in intelligent decision support mechanism to compare with optimal ranges of values, and to message the appropriate alarming signals as per the severity of the deviation. The system developed was tested with a prototype model comprising the Internet of Things, internet communication technology, and a machine learning algorithm, MEMS with standard input, output, storage and display of components, which was developed in a laboratory but implemented in a case study in a real industrial production plant. After the successful implementation of the developed system, the performance of the critical machine was evaluated in terms of metrics such as the average number of failures, average downtime and average service time spent. It was found that after the implementation, the downtime has decreased by nearly 22% and for the performance in terms of its output, the flow rate exhibited a steady increase with the passage of time.
Keywords: industrial internet of things; machine learning algorithm; anomaly detection; optimal decision-making process
*Corresponding author: Tel.: (+91) 8344349940
E-mail: velmurugan2601@gmail.com
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Transfer Statement
The copyright of this article is transferred to Current Applied Science and Technology journal with effect if and when the article is accepted for publication. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, electronic form (offline, online) or any other reproductions of similar nature.
The author warrants that this contribution is original and that he/she has full power to make this grant. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors.
Here is the link for download: Copyright transfer form.pdf
References
Yogitha, K. and Alamelumangai, V., 2016. Recent trends and issues in IoT. International Journal of Advances in Engineering Research, 11(I), 50-56.
Patil, A.A. and Suralkar D.S., 2017. Review on-IOT based smart healthcare system. International Journal of Advanced Research in Engineering and Technology, 8(3), 37-42.
Byington, C.S., Watson, M., Edwards, D. and Dunkin, B., 2003. In-line health monitoring system for hydraulic pumps and motor. 2003 IEEE Aerospace Conference Proceedings, MT, USA, March 8-15, 2003, pp. 3279-3287.
Fenton, N., Neil, M., Marsh, W., Hearty, P., Radliński, L. and Krause, P., 2008. On the effectiveness of early life cycle defect prediction with Bayesian nets. Empirical Software Engineering, 13(5), DOI: 10.1007/s10664-008-9072-x.
Yang, Q., Li, J., Blond, S.L. and Wang, C., 2016. Artificial neural network-based fault detection and fault location in the DC microgrid. Energy Procedia, 103, 129-134, DOI: 10.1016/j.egypro.2016.11.261.
Farokhzad, S., 2013. Vibration-based fault detection of the centrifugal pump by fast Fourier transform and adaptive neuro-fuzzy inference system. Journal of Mechanical Engineering and Technology, 1(3), 82-87.
Murthy, A.S.R., Priya., Ram, Y.K.V. and Sai, K.K., 2018. Fault. Diagnosis of pump-motors by using condition monitoring and IoT technique. International Journal of Mechanical Engineering and Technology, 9(4), 828-836.
Mohanty, A.R., Pradhan, P.K., Mahalik, N.P. and Dastidar, S.G., 2012. Fault detection in a centrifugal pump using vibration and motor current signature analysis. International Journal of Automation and Control, 6(3-4), 261-276.
Prakash, S., Manoj, H.A.N.S. and Thorat, V., 2021. Automatic fault localization and isolation in power distribution network by decision support method. Walailak Journal of Science and Technology, 18(4), 8982-8988, DOI: 10.48048/wjst.2021.8982.
Sarwar, M., Mehmood, F., Abid, M., Khan, A.Q., Gul, S.T. and Khan, A.S., 2019. High impedance fault detection and isolation in power distribution networks using support vector machines. Journal of King Saud University-Engineering Sciences, 32(8), 524-535, DOI: 10.1016/j.jksues.2019.07.001.
Sanayha, M. and Vateekul, P., 2019. Remaining useful life prediction using enhanced convolutional neural network on multivariate time series sensor data. Walailak Journal of Science and Technology, 16(9), 669-679, DOI: 10.48048/wjst.2019.4144.
Xue, H., Li, Z., Wang, H. and Chen, P., 2014. Intelligent diagnosis method for centrifugal pump system using vibration signal and support vector machine. Shock and Vibration, 2014, DOI: 10.1155/2014/407570.
Pitchipoo, P., Ponnusamy, V. and Sivaprakasam, R., 2013 Fuzzy hybrid decision model for supplier evaluation and selection. International Journal of Production Research, 13, 3903-3919, DOI: 10.1080/00207543.2012.756592.
Pandian, R.S. and Soltysova, Z., 2018. Management of mass customized orders using flexible schedules to minimize delivery times. Polish Journal of Management Studies, 18, DOI: 10.17512/pjms.2018.18.1.19.
Pandian, R., Sudhakara, Robert, S., Dhanashri, V. and Katarzyna, C., 2020. Management of non-value-added activities to minimize lead time using value stream mapping in the steel industry. Acta Montanistica Slovaca, 25(3), 444-454.
Rajakarunakaran, S., Venkumar, P., Devaraj, D., and Rao, K.S.P., 2008. Artificial neural network approach for fault detection in rotary system. Applied Soft Computing, 8(1), 740-748, DOI: 10.1016/j.asoc.2007.06.002.
Aungkulanon, P., Phruksaphanrat, B. and Luangpaiboon, P., 2020. Dynamic maintenance scheduling with Fuzzy data via biogeography-based optimization algorithm and its hybridizations. Current Applied Science and Technology, 20(2), 226-237.
Klinchaeam, S., Nivesrangsan, P. and Lokitsangthong, M., 2009. Condition monitoring of a small four-stroke petrol engine using vibration signals. Current Applied Science and Technology 9(1), 9-17.
Abass, S.A. and Elsayed, M.S., 2012. On the application of uncertainty models in copying machine maintenance problem. Current Applied Science and Technology, 12(1), 62-76.
Chanmee, S. and Kesorn, K., 2020. Data quality enhancement for decision tree algorithm using knowledge-based model. Current Applied Science and Technology, 20(2), 259-277.
Adeyanju, I.A., Emake, E.D., Olaniyan, O.M., Omidiora, E.O., Adefarati, T., Uzedhe, G.O. and Okomba, N.S., 2021. Digital industrial control systems: Vulnerabilities and security technologies. Current Applied Science and Technology, 21(1), 188-207.
Vimuttasoongviriya, A., Kwatra, N. and Kumar, M., 2011. Vibration monitoring and damage assessment of a retrofitted RC frame model. Current Applied Science and Technology, 11(2), 43-53.
Velmurugan, K., Saravanasankar, S. and Bathrinath, S., 2022. Smart maintenance management approach: Critical review of present practices and future trends in SMEs 4.0. Materials Today: Proceedings, 62(6), 2988-2995, DOI: 10.1016/j.matpr.2022.02.622.
Velmurugan, K., Saravanasankar, S., Venkumar, P., Sudhakarapandian, R. and Di, B.G., 2022. Hybrid fuzzy AHP-TOPSIS framework on human error factor analysis: Implications to developing optimal maintenance management system in the SMEs. Sustainable Futures, 4, DOI: 10.1016/j.sftr.2022.100087.
Velmurugan, K., Venkumar, P. and Sudhakarapandian, R., 2019. Design of optimal maintenance policy using Markov model. International Journal of Engineering and Advanced Technology, 9, 907-917, DOI: 10.35940/ijeat.A1068.1291S419.
Velmurugan, K, Venkumar, P. and Sudhakarapandian, R., 2019. Reliability availability maintainability analysis in forming industry. International Journal of Engineering and Advanced Technology, 9, 822-828, DOI: 10.35940/ijeat.A1049.1291S419.
Velmurugan, K., Venkumar, P. and Sudhakarapandian, R., 2021. Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach. Mathematical Problems in Engineering, 2021, DOI: 10.1155/2021/6616037.
Velmurugan, K., Venkumar, P. and Sudhakara, P.R., 2021. SME 4.0: Machine learning framework for real-time machine health monitoring system. Journal of Physics: Conference Series,1911, DOI: 10.1088/1742-6596/1911/1/012026.
Kumaran, E.M., Velmurugan, K., Venkumar, P., Guka, D.A. and Divya, V., 2022. Artificial intelligence-enabled IoT-based smart blood banking system. Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications: ICAIAA 2021, Jaipur, India, March 27-28, 2021, pp.119-130.