RESEARCH PAPER
Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings
| 1 | Center for Risk Analysis and Environmental Modeling – CEERMA Department of Production Engineering Universidade Federal de Pernambuco – UFPE Av. Prof. Moraes Rego 1235 – University City Recife – PE – Brazil – 50670-901 |
| 2 | Department of Production Engineering Universidade Federal de Pernambuco – UFPE Av. Prof. Moraes Rego 1235 – University City Recife – PE – Brazil – 50670-901 |
| 3 | marcio@ceerma.org |
| 4 | Center for Risk Analysis and Environmental Modeling – CEERMA |
Publication date: 2019-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(4):610–618
KEYWORDS
big datavibration signalbearingsremaining useful lifeempirical mode decompositionwaveletstransformsupport vector machineparticle swarm optimization
ABSTRACT
The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several
competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm
optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to
predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the
complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both
pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models
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