# Detection and Monitoring of Fitting Progression on Gear Tooth Flank Using Deep Learning
## 1 Introduction  
Gears, as essential mechanical elements of transmissions, are a subject of intensive research that ranges from quality control of their manufacturing [1], via optimization [2, 3, 4], estimation of load capacity [5, 6], and application of various types of materials [7], to thermal effects in their operation [8]. The operation of gearboxes is based on the contact of gear teeth. This contact involves high pressures that can lead to surface damage of gear teeth. Calculations of gear load carrying capacities are made so that a gear set can work appropriately for the required operation time. High contact pressures can lead to the removal of the material surface during the stroke of two gear wheels. One of the most common types of gear surface damage is pitting. Pitting is followed by a remarkable rise in noise, which can cause a gear to become unworkable. The integration of fault diagnostics systems that can detect and monitor pitting progression can help prevent catastrophic failures in advance and reduce maintenance costs. There are many studies on the detection of pitting by using vibration or acoustic analysis. [PERSON] [9] presented pitting detection in the case of a worm gearbox with vibration analysis. He used the spectral kurtosis and enveloping technique to identify the presence of defects. [PERSON] [10] used vibration analysis for early detection and progression monitoring of pitting on gears with large helical transmission used in a ball-mill operation, analyzed using basic visual inspection. [PERSON] [11] proposed using digital holographic surface imaging to observe the electrode surface during a pitting corrosion process. Thismethod provides visual evidence of localized dissolution on the electrode surface and offers an approach to monitoring pitting progression in real time. [PERSON] [12; 13] proposed a method for early gear pitting fault diagnosis based on the raw vibration signal as a direct input. The method was developed by stacking a sparse autoencoder and a Gauss-Binary restricted Boltzmann machine. [PERSON] [14] proposed an unsupervised feature extraction method to learn fault levels directly from the frequency spectra of the data. Test data were collected on seeded pitting faults. The results show that the proposed method can be used to identify faults and for real-time fault diagnosis and prognosis. [PERSON] [15] investigated the tooth pitting propagation of a spur gear using model-based analysis. He used his model to estimate pitting growth by investigating effects on vibration properties. [PERSON] [16; 17] carried out an experimental study with spur gears and detection of pitting with acoustic and vibration sensors. Through the early detection of pitting damage, he proved that it is possible to predict severe pitting a few hours before it actually occurs. In recent times, researchers have started using machine vision and deep learning to detect pitting on the tooth flank. [PERSON] [18] suggested a method for evaluating gear damage by pitting. She proposed and tested her setup by taking photos from the gear tooth surface and employing the macrophoto method to evaluate pitting. [PERSON] [19] implemented an inspection system that uses a faster R-CNN network to identify teeth defects after the manufacturing process and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%. [PERSON] [20] proposed a method for measuring the area ratio of tooth pitting that is based on a deep convolutional generative adversarial network (DCGAN) and a fully convolutional segmentation network (U-Net). DCGAN is applied to expand pitting samples. [PERSON] [21] proposed a real-time detection of gear tooth pitting based on machine vision. He used a principle of gear meshing and the shooting principle of a line-scan camera in order to obtain the optimal centrifugal shooting distance for further analysis. [PERSON] [22; 23; 24] enlarged training samples with multilevel pitting.