Akademik Veri Yönetim Sistemi
Eksploatacja I Niezawodnosc-Maintenance And Reliability, cilt.22, sa.3, ss.544-556, 2020 (SCI-Expanded)
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.