Prediksi Keandalan Sistem Pendingin Berdasarkan Kerusakan Sistem Dengan Menggunakan Distribusi Probabilitas Poisson

Mohammad Zainuddin, Verra Aullia, Arbain Arbain, Supriadi Supriadi

Abstract


Operasi suatu unit/sistem yang tidak disertai dengan pemeliharaan yang baik akan berakibat pada turunnya reliability unit/sistem dimaksud. Jika failure (kegagalan fungsi) unit/sistem dimaksud dapat diprediksi dengan baik maka pemeliharaan akan dapat direncanakan dengan baik pula. Prediksi failure sangat berguna bagi diterapkannya Preventive Maintenance (PM) atau Conditional Based Maintenance (CBM). Prediksi failure didasarkan pada ketersediaan data historis failure suatu unit/sistem yang cukup. Metode yang digunakan untuk memprediksi failure adalah Random Poisson. Rata-rata hasil simulasi pembangkitan pola failure menggunakan random Poisson digunakan sebagai output prediksi kejadian failure di periode berikutnya. Hasil prediksi yang diperoleh akan menjadi dasar untuk memperkirakan reliability sistem di periode dimaksud.

Keywords


reliability; failure; Random Poisson; prediksi

Full Text:

PDF

References


G. P. Sullivan, R. Pugh, A. P. Melendez et al., "Operations &

Maintenance Release 3.0: Operations & Maintenance - Best Practices,"

A Guide to Achieving Operational Efficiency, Federal Energy

Management Program - U.S. Department of Energy, 2010.

F. S. Dhillon, “EFECT OF PREVENTIVE MAINTENANCE IN

INDUSTRIES,” International Journal In Applied Studies And

Production Management, vol. 2, no. 3, pp. 63-68, 2016.

L. Topliceanu, P. Gabriel, and I. Furdu, “Functional Problems and

Maintenance Operations of Hydraulic Turbines,” TEM JOURNAL -

Technology, Education, Management, Informatics, vol. 5, no. 1, pp. 32–

, 2016.

D. Isaacs, A. Astarola, J. Diaz et al., “Making Factories Smarter

Through Machine Learning,” IIC Journal of Innovation 2017.

S. Munirathinam, and B. Ramadoss, “Predictive Models for Equipment

Fault Detection in the Semiconductor Manufacturing Process,”

International Journal of Engineering and Technology, vol. 8, no. 4, pp.

-285, 2016.

H. Wang, X. Ye, and M. Yin, “Study on Predictive Maintenance

Strategy,” International Journal of u- and e- Service, Science and

Technology, vol. 9, no. 4, pp. 295-300, 2016.

N. Pancholi, and M.G.Bhatt, “Performance Reliability Improvement by

Optimizing Maintenance Practices through Failure Analysis in Process

Industry –A Comprehensive Literature Review,” IOSR Journal of

Mechanical and Civil Engineering (IOSR-JMCE), vol. 13, no. 6, pp. 66-

, 2016.

A. P. Rifai, H.-T. Nguyen, and S. Z. M. Dawal, “Multi-objective

adaptive large neighborhood search for distributed reentrant permutation

flow shop scheduling,” Applied Soft Computing, vol. 40, pp. 42-57,

N. Banduka, I. Veža, and B. Bilić, “An integrated lean approach to

Process Failure Mode and Effect Analysis (PFMEA): A case study from

automotive industry,” Advances in Production Engineering &

Management, vol. 11, no. 4, pp. 355-365, 2016.

M. A. Geetha, and R. S. P. Kumar, “Effective Estimation of Total

Failure Mode Effects and Analysis in Tea Industry,” Asian Journal of

Information Technology, vol. 15, no. 20, pp. 4030-4039, 2016.

D. Zhou, Y. Tang, and W. Jiang, “A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory,” Mathematical Problems in Engineering, vol. 2016, pp. 1-11, 2016.

X. Deng, and W. Jiang, “Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method,” Sensors, vol. 17, no. 12, pp. 2086, 2017.

M. S. Upadhya, “Fuzzy Logic-Based Failure Mode Effect and Criticality Analysis –A Case Study of Water Filters of a Company,” Journal of Computer Applications (JCA), vol. 6, no. 4, pp. 89-93, 2013.

A. .R, Priyanka, and R. M. .Patil, “Health Monitoring In Aerospace System,” International Journal of Informative & Futuristic Research vol. 4, no. 9, pp. 7556-7561, 2017.

P. Pillai, A. Kaushik, S. Bhavikatti et al., “A Hybrid Approach for Fusing Physics and Data for Failure Prediction,” International Journal of Prognostics and Health Management, 2016.

L. Zhang, “Big Data Analytics for Fault Detection and its Application in Maintenance - Doctoral Thesis,” Department of Operation and Maintenance Engineering, University of Technology, Lulea- Sweden, 2016.

B. Hajek, "Random Processes for Engineers," 2015].

H. P. Hsu, Theory and Problems of Probability, Random Variables, and Random Processes: The McGraw-Hill Companies, Inc, 2012.

E. F. Saraiva, A. K. Suzuki, C. A. O. Filho et al., “Predicting football scores via Poisson regression model: applications to the National Football League,” Communications for Statistical Applications and Methods, vol. 23, no. 4, pp. 297-319, 2016.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.