Design of ANFIS system to detect the condition of generator set model P22-6 based on Omron CJ1M PLC

Main Article Content

Nanda Putri Rahmawati
Ryan Yudha Adhitya
Hendro Agus Widodo
Afif Zuhri Afianto
Agus Khumaidi
Ratna Budiawati
Fitri Hardiyanti
Mochamad Yusuf Santoso

Abstract

The application of machine monitoring systems is currently increasingly needed, one of which is on generators. Generator sets are one of the important elements in providing energy needed in company operations. However, to ensure optimal performance and prevent unexpected engine damage, careful monitoring of the generator set's operational conditions is required, especially of key variables such as temperature, rotation speed, and engine vibration. The purpose of this study is to identify the condition of the generator set using three parameters. In this research, adaptive neuro fuzzy inference system (ANFIS) is used as a tool to model the relationship between inputs (temperature, speed, and vibration) and outputs (engine condition). The dataset for normal conditions amounted to 25 data and for abnormal conditions amounted to 25 data. From this data, an RMSE of 0.000032 was obtained in the 3-3-5 membership function structure with a trapezoidal type membership function. And at the stage of applying fuzzy to the Omron PLC, the RMSE is 0. Simulations are carried out to test the effectiveness of ANFIS in predicting machine conditions based on monitored parameters.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
N. P. Rahmawati, “Design of ANFIS system to detect the condition of generator set model P22-6 based on Omron CJ1M PLC”, J. Soft Comput. Explor., vol. 5, no. 3, pp. 240-250, Sep. 2024.
Section
Articles

References

B. S. Hartono, P. M. Bambang, B. M. Wahyu, and A. Pudin, “Development of generator set operation monitoring system for performance analysis and periodic maintenance based on IoT technology,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2020. doi: 10.1088/1757-899X/830/2/022085.

S. Das, B. Nayak, S. K. Sarangi, and D. K. Biswal, “Condition Monitoring of Robust Damage of Cantilever Shaft Using Experimental and Adaptive Neuro-fuzzy Inference System (ANFIS),” in Procedia Engineering, Elsevier Ltd, 2016, pp. 328–335. doi: 10.1016/j.proeng.2016.05.140.

V. Vincentdo and N. Surantha, “Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS,” Electronics (Switzerland), vol. 12, no. 6, Mar. 2023, doi: 10.3390/electronics12061446.

H. D. Bhakti and H. Abror, “Aplikasi Adaptive Neuro Fuzzy System (ANFIS) Untuk Mem-prediksi Kebutuhan Gas Bumi Indonesia,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 2, pp. 73–84, Aug. 2022, doi: 10.35746/jtim.v4i2.198.

M. I. Azhar and W. Firdaus Mahmudy, “Prediksi Curah Hujan Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS),” 2018. [Online]. Available: http://j-ptiik.ub.ac.id

S. Aprilia Hardiyanti, Q. Shofiyah, J. Teknik Sipil, P. Negeri Banyuwangi, and J. K. Raya Jember, “PREDIKSI KASUS COVID-19 DI INDONESIA MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS),” Seminar Nasional Terapan Riset Inovatif (SENTRINOV) Ke-6 ISAS Publishing Series: Engineering and Science, vol. 6, no. 1, 2020.

F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.

S. Kumar and M. Singh, “Big data analytics for healthcare industry: Impact, applications, and tools,” Big Data Min. Anal., vol. 2, no. 1, pp. 48–57, 2019, doi: 10.26599/BDMA.2018.9020031.

L. M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, “Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges,” IEEE Access, vol. 7, pp. 6473–6492, 2019, doi: 10.1109/ACCESS.2018.2887076.

B. P. L. Lau et al., “A survey of data fusion in smart city applications,” Inf. Fusion, vol. 52, no. January, pp. 357–374, 2019, doi: 10.1016/j.inffus.2019.05.004.

Y. Wu et al., “Large scale incremental learning,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 374–382, 2019, doi: 10.1109/CVPR.2019.00046.

A. Mosavi, S. Shamshirband, E. Salwana, K. wing Chau, and J. H. M. Tah, “Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning,” Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 482–492, 2019, doi: 10.1080/19942060.2019.1613448.

V. Palanisamy and R. Thirunavukarasu, “Implications of big data analytics in developing healthcare frameworks – A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 31, no. 4, pp. 415–425, 2019, doi: 10.1016/j.jksuci.2017.12.007.

J. Sadowski, “When data is capital: Datafication, accumulation, and extraction,” Big Data Soc., vol. 6, no. 1, pp. 1–12, 2019, doi: 10.1177/2053951718820549.

J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, “Comparing a traditional approach for financial brand communication analysis with a big data analytics technique,” IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301.

D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4679–4690, 2019, doi: 10.1109/TITS.2019.2924883.

S. Schulz, M. Becker, M. R. Groseclose, S. Schadt, and C. Hopf, “Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development,” Curr. Opin. Biotechnol., vol. 55, pp. 51–59, 2019, doi: 10.1016/j.copbio.2018.08.003.

C. Shang and F. You, “Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era,” Engineering, vol. 5, no. 6, pp. 1010–1016, 2019, doi: 10.1016/j.eng.2019.01.019.

Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, “Clinical big data and deep learning: Applications, challenges, and future outlooks,” Big Data Min. Anal., vol. 2, no. 4, pp. 288–305, 2019, doi: 10.26599/BDMA.2019.9020007.

M. Huang, W. Liu, T. Wang, H. Song, X. Li, and A. Liu, “A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks,” IEEE Access, vol. 7, pp. 23816–23833, 2019, doi: 10.1109/ACCESS.2019.2899402.

Abstract viewed = 23 times