Abstract
Performance degradation assessment is an essential assessment in a rotating machine, especially for condition-based maintenance (CBM) and predictive maintenance, thus need a system that can predict machine failure. This paper proposes particle swarm optimization as a method to predict the remaining useful life of machine failure. For this purpose, motor current and vibration of Kurtosis value were determined as input data, while an acoustic emission signal of Kurtosis value as an output data to be analyzed. Machine breakdown remaining time was decided by AC drop until it close to zero (0) value. To sum up, this proposed method was successfully developed to predict the remaining useful life of machine failure.
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References
Yang L, Wang F, Zhang J, Ren W (2019) Remaining useful life prediction of ultrasonic motor based on elman neural network with improved particle swarm optimization. Meas J Int Meas Conf 143:27–38
Zhang B, Zhang S, Li W (2019) Bearing performance degradation assessment using long short-term memory recurrent network. Comput Ind 106:14–29
Rashid NA, Abdul Aziz I, Hasan MHB (2019) Machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm. In: Silhavy R (ed) Artificial intelligence methods in intelligent algorithms. Advances in intelligent systems and computing, vol 985. Springer, Cham
Jiao X, Jing B, Li J, Sun M, Wang Y (2018) Research on remaining useful life prediction of fuel pump based on adaptive differential evaluation grey wolf optimizer-support vector machine. Yi Qi Yi Biao Xue Bao/Chin J Sci Instrum 39(8):43–52
Maior CBS, Moura MDC, Lins ID (2019) Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings. Eksploatacja i Niezawodnosc 21(4):610–619
Li Z, Goebel K, Wu D (2019) Degradation modeling and remaining useful life prediction of aircraft engines using ensemble learning. J Eng Gas Turb Power 141(4)
GarcÃa Nieto PJ, GarcÃa-Gonzalo E, Sánchez Lasheras F, De Cos Juez FJ (2015) Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliabil Eng Syst Saf 138:219–231. https://doi.org/10.1016/j.ress.2015.02.001
El Afia A, Sarhani M (2017) Particle swarm optimization for model selection of aircraft maintenance predictive models. In: Paper presented at the ACM international conference proceeding series, Part F129474
Debruyne S, van Massenhove K, Brackx K (2020) Real-time underwater abrasive water jet cutting process control. Euronoise 2015:1375–1379
Real-time Condition-based Maintenance (2007)
Lee J, Qiu H, Yu G, Lin J (2007) Bearing data set NASA ames prognostics data repository. Rexnord Technical Services, IMS, University of Cincinnati
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Tahir, M.N., Mahamad, A.K., Saon, S., Sathasivam, S., Ameen, H.A. (2022). Machine Remaining Useful Life (RUL) Prediction Based on Particle Swarm Optimization (PSO). In: Isa, K., et al. Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. Lecture Notes in Electrical Engineering, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-16-2406-3_46
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DOI: https://doi.org/10.1007/978-981-16-2406-3_46
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