Worm gear condition monitoring and fault detection from thermal images via deep learning method

• Autorzy: Karabacak Yunus Emre , Gürsel Özmen Nurhan , Gümüşel Levent

Identyfikatory

DOI

10.17531/ein.2020.3.18

Warianty tytułu

PL

Monitorowanie stanu i wykrywanie błędów przekładni ślimakowej na podstawie termogramów z wykorzystaniem metody głębokiego uczenia

• Języki publikacji: EN

Abstrakty

EN

Worm gearboxes (WG) are often preferred, because of their high torque, quickly reducing speed capacity and good meshing effectiveness, in many industrial applications. However, WGs may face with some serious problems like high temperature at the speed reducer, gear wearing, pitting, scoring, fractures and damages. In order to prevent any damage, loss of time and money, it is an important issue to detect and classify the faults of WGs and develop the maintenance plans accordingly. The present study addresses the application of the deep learning method, convolutional neural network (CNN), in the field of thermal imaging that were gathered from a test rig operating on different loads and speeds. Deep learning approaches, have proven their powerful capability to exploit faulty information from big data and make intelligently diagnostic decisions. Studies concerning the condition monitoring of WGs in the literature are limited. This is the first study on WGs with infrared thermography rather than vibration and sound measurements which have some deficiencies about hardware requirements, restricted measurement abilities and noisy signals. For comparison, CNN was also trained, with vibration and sound data which were collected from the healthy and faulty WGs. The results of fault diagnosis show that thermal image based CNN model on WG has achieved 100% success rate whereas the vibration performance was 83.3 % and sound performance was 81.7%. As a result, thermal image based CNN model showed a better diagnosing performance than the others for WGs. Moreover, condition monitoring of WGs, can be performed correctly with less measurement costs via thermal imaging methods.

PL

W wielu zastosowaniach przemysłowych preferuje się przekładnie ślimakowe, ze względu na ich wysoki moment obrotowy, możliwość szybkiej redukcji prędkości i dobrą sprawność zazębienia. Jednakże przekładnie tego typu narażone są często na poważne problemy, takie jak wysoka temperatura przy reduktorze prędkości czy też zużycie, pitting (wżery), zatarcie, pęknięcie lub uszkodzenie kół zębatych. Zapobiec takim uszkodzeniom, i związanym z nimi stratom finansowym i czasowym, można poprzez wykrywanie i klasyfikowanie błędów przekładni i odpowiednie opracowanie planów konserwacji. Niniejsze badanie dotyczy zastosowania metody głębokiego uczenia oraz splotowych sieci neuronowych (SSN) do monitoringu stanu przekładni na podstawie termogramów zarejestrowanych na stanowisku testowym pracującym przy różnych obciążeniach i prędkościach. Podejścia oparte na uczeniu głębokim umożliwiają efektywne wykorzystanie informacji o błędach pochodzących z dużych zbiorów danych i podejmowanie trafnych decyzji diagnostycznych. Niewiele z dostępnych publikacji poświęconych jest monitorowaniu stanu przekładni ślimakowych. Niniejsza praca jako pierwsza przedstawia badania przekładni ślimakowej z zastosowaniem termografii zamiast zwyczajowo prowadzonych pomiarów drgań i dźwięku, które mają pewne wady dotyczące wymagań sprzętowych, ograniczonych możliwości pomiarowych i głośności sygnałów. SNN opartą na danych termicznych porównano z siecią, którą uczono na zbiorach danych wibracyjnych i akustycznych pochodzących z prawidłowo działających i uszkodzonych przekładni ślimakowych. Wyniki diagnostyki uszkodzeń pokazują, że model SSN przekładni ślimakowej oparty na obrazie termicznym osiągnął stuprocentową (100%) skuteczność, podczas gdy skuteczność modeli opartych na danych wibracyjnych i akustycznych wyniosła, odpowiednio, 83,3% i 81,7%. Tym samym, model SNN oparty na obrazie termicznym pozwalał na trafniejsze diagnozowanie przekładni ślimakowej niż pozostałe modele. Ponadto zastosowanie metod opartych na termografii pozwala na poprawne monitorowanie stanu przy niższych kosztach pomiaru.

Słowa kluczowe

EN

fault diagnosis; worm gears; thermal imaging; convolutional neural networks; GoogLeNet; condition monitoring

PL

diagnostyka błędów; przekładnie ślimakowe; termografia; splotowe sieci neuronowe; GoogLeNet; monitorowanie stanu

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2020
• Tom: Vol. 22, no. 3
• Strony: 544--556
• Opis fizyczny: Bibliogr. 51 poz., rys., tab.

Twórcy

autor

Karabacak Yunus Emre

• General Directorate of Tea Enterprises 53080 Rize, Turkey

autor

Gürsel Özmen Nurhan

• Karadeniz Technical University, Mechanical Engineering Department 61080 Trabzon, Turkey

autor

Gümüşel Levent

• Karadeniz Technical University, Mechanical Engineering Department 61080 Trabzon, Turkey

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