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

Abstract

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.

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

“…In reference to Figure 11 and Table 9, a hierarchy structure model of the fault propagation intensity of a machining centre can be obtained as shown in Figure 12. In reference to Equation (16) and Figure 11, the fault propagation probability values of each path in the fault propagation hierarchy model of a machining centre at 1500 h can be calculated as shown in Table 10. As expressed in Table 10, the fault propagation probability of each path is greater than the threshold value of 10 −8 ; thus, a fault propagation phenomenon exists in the model.…”

“…The current fault diagnosis methods can be summarized into four categories [8,9]: knowledge-based fault diagnosis [10][11][12], model-based fault diagnosis [13][14][15], signalbased fault diagnosis [16][17][18], and hybrid method-based fault diagnosis (a method that combines two or more methods) [19][20][21][22]. Fault diagnosis for machining centres mainly include diagnosis methods based on fault information monitoring, training models, and fault trees.…”

Thermographic Fault Diagnosis of Shaft of BLDC Motor

“…The analysis of thermal images is presented in the literature [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Worm gear condition monitoring using thermal images was presented in reference [ 1 ]. Infrared thermal images were analyzed using a convolutional neural network.…”

The Simulation-Based Approach for Random Speckle Pattern Representation in Synthetically Generated Video Sequences of Dynamic Phenomena

“…Image processing algorithms and computer vision systems have become essential tools for inspecting and monitoring structures [ 1 ] in the fields of mechanical [ 2 ], aviation [ 3 ], and civil engineering applications [ 4 , 5 ]. Image data contains full-field displacement and deformations of objects of interest.…”