# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
## 1 Introduction
[14] conducted a comprehensive literature review. As a result, the study reveals that vibration measurement was the most effective and reliable method for detecting errors in gearboxes. [PERSON] et al. [15] conducted experimental studies to detect root cracks in spur gears. In the study, gears with different damage levels (10%-20%-30%-40%-50%) were examined at variable speeds and loading conditions. Vibration data were collected in the time domain through the experimental system. The collected data were extracted by the recommended time domain averaging method. By calculating the RMS, kurtosis, and crest factor values of the extracted data, the damage detection of the root cracks observed in spur gears was carried out. [PERSON] et al. [16] developed a new signal processing method for failure detection and monitoring in gearboxes. A single-stage gearbox was used in the experiments, and acceleration values were measured at constant speeds for healthy and damaged gears. Using the developed signal processing method, they attempted to determine error levels in gears. [PERSON] et al. [17] used the acoustic emission method to detect local errors in planetary gear mechanisms. Failure detection was carried out by applying the wavelet transform method to the acoustic emission signals collected through the established experimental setup. It was observed that the acoustic emission method gave better results than the vibration method in detecting local errors. [PERSON] [18] created a dynamic model with 6-DOF to detect failures in spur gears. By running the dynamic model, the vibration changes in the x- and y-directions. Vibration was observed to be higher for faulty gears in each revolution than for healthy gears. [PERSON] [19] developed a 6-DOF dynamic model to investigate the crack detection features of the HCR spur gears. Vibration signals were collected from the dynamic model, and statistical indicators were used to evaluate the vibration signal. When the crack size increases, the indicators increase less for the HCR design than for the LCR. [PERSON] et al. [20] detected the tooth root cracks for spur gears with asymmetric teeth using the dynamic transmission error signals from the developed 6-DOF dynamic model. When the drive side pressure angle increased, the crack detection became harder than in the symmetric case. Statistical indicators are data elements that represent statistical data for a specific time, place, and other characteristics to enable the comparison of meaningful data. When the literature is examined, it is seen that many researchers detect various failures for gears and other machine elements by using statistical indicators. [PERSON] et al. (2018) created a dynamic model to detect crack errors occurring in planetary mechanisms. The author detected the existence of the error by using the change of the RMS indicator for the acceleration and displacement signals obtained from the dynamic model. [PERSON] et al. (2018) analyzed the displacement signals obtained from eight and six degrees of freedom dynamic models with the help of kurtosis and RMS indicators. The failure levels for gear cracks were determined to correspond to the percentage changes in the indicators. [PERSON] et al. (2018) developed a numerical model for detecting cracks in gear cracks. Mesh stiffnesses for gears with different failure levels were calculated analytically and used in a six degrees of freedom dynamic model to obtain vibration signals for each failure level. The degree of failure was determined using kurtosis and RMS indicators of the vibration signals. [PERSON] et al. (2018) evaluated the dynamic transmission error and vibration signals with the help of three different indicators to detect the amount of cracking in asymmetric spur gears. The amount of cracking was determined by looking at the changes in kurtosis, RMS, and crest factor indicators for signals with different error levels. The crest factor was found to be the most influential parameter.