Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings

Souto, Maior Caio Bezerra; das Chagas Moura, Márcio; Lins, Isis Didier

doi:10.17531/ein.2019.4.10

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Tytuł artykułu

Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings

Autorzy

Souto Maior Caio Bezerra , das Chagas Moura Márcio , Lins Isis Didier

Treść / Zawartość

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DOI

10.17531/ein.2019.4.10

Warianty tytułu

Zastosowanie maszyn wektorów nośnych zoptymalizowanych metodą roju cząstek oraz technik przetwarzania wstępnego do oceny pozostałego okresu użytkowania łożysk

Języki publikacji

Abstrakty

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.

Okres użytkowania sprzętu jest ważną zmienną związaną z prognozowaniem pracy systemu, a możliwość jego dokładnej oceny daje zakładom przemysłowym znaczną przewagę konkurencyjną. W tym artykule pozostały czas pracy (Remaining Useful Life, RUL) szacowano za pomocą maszyn wektorów nośnych zoptymalizowanych rojem cząstek (SVM+PSO) z uwzględnieniem dwóch technik przetwarzania wstępnego pozwalających na poprawę jakości danych wejściowych: empirycznej dekompozycji sygnału (Empirical Mode Decomposition, EMD) oraz transformat falkowych (Wavelet Transforms, WT). W niniejszej pracy, EMD i falki w połączeniu z SVM wykorzystano do prognozowania RUL łożyska ze zbioru danych IEEE PHM Challenge 2012 Big Dataset. W szczególności, przeanalizowano dwa przypadki: uwzględniający kompletny zestaw danych o drganiach oraz drugi, biorący pod uwagę okrojoną wersję tego zbioru. Prognozy otrzymane na podstawie modeli, w których zastosowano obie techniki przetwarzania wstępnego porównano z wynikami uzyskanymi za pomocą PSO + SVM bez wstępnego przetwarzania danych. Wyniki pokazały, że model EMD + SVM generował dokładniejsze prognozy i tym samym przewyższał pozostałe badane modele.

Słowa kluczowe

big data vibration signal bearings remaining useful life empirical mode decomposition wavelets transform support vector machine particle swarm optimization (PSO)

zbiór danych sygnał drgań łożyska pozostały okres użytkowania empiryczna dekompozycja sygnału transformata falkowa maszyna wektorów nośnych optymalizacja rojem cząstek

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2019

Tom

Vol. 21, no. 4

Strony

610--618

Opis fizyczny

Bibliogr. 60 poz., rys., tab.

Twórcy

autor

Souto Maior Caio Bezerra

caio.maior@ufpe.br

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
Department of Production Engineering Universidade Federal de Pernambuco – UFPE Av. Prof. Moraes Rego 1235 – University City Recife – PE – Brazil – 50670-901

autor

das Chagas Moura Márcio

marcio@ceerma.org

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
marcio@ceerma.org
Center for Risk Analysis and Environmental Modeling – CEERMA

autor

Lins Isis Didier

isis.lins@ceerma.org

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
Center for Risk Analysis and Environmental Modeling – CEERMA

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