# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 1 Introduction  
Contact fatigue is mainly due to damage caused by cyclic loading below the contact surface, until it extends to the surface to form pitting or spalling. The failure process is essentially the initiation and propagation of cracks [1, 2]. A large number of engineering practices have proved that contact fatigue is the main reason for the failure of basic components such as bearings and gears [3]. At present, the main way to obtain the contact fatigue properties of materials is through rolling contact fatigue tests. Therefore, it is very important to accurately evaluate the contact fatigue state of the specimen during the test for the accurate acquisition of the material contact fatigue properties. At present, there are two methods to realize the online state identification of the specimen contact fatigue state. (1) It can be directly identified according to the damage area of the specimen. For example, [PERSON] et al. [4] applied machine vision technology to the rolling contact fatigue test bench for the first time. Although the study used a line scan camera to obtain the surface image of the specimen and observed the evolution of fatigue damage, it was still only based on the traditional vibration analysis to determine the fatigue failure. [PERSON] et al. [5] used an area scan camera to obtain surface images of rolling contact fatigue specimen and compared the surface roughness of the specimen under different rotational speeds to determine whether they failed. However, the image stitching effect is not ideal, which directly affects the observation of the surface morphologyof the specimen. Moreover, only the experiments under dry friction conditions were carried out, and the influence of lubricating medium on image acquisition under actual operating conditions was not considered. Until now, most of the existing testing machines cannot obtain online images of the contact surface of the specimen, let alone quantify the damaged area and track the damage evolution process. Therefore, our team independently developed a rolling contact fatigue test equipment based on machine vision technology, which solved this problem [6]. However, during the test, it was found that the change of the test environment had a great influence on the acquisition of fatigue damage images, especially in the later stage of the test. The oil blocking effect of the oil blocking device is poor, or the lens is splashed with oil stains, which will cause the image to become black, thus affecting the accurate acquisition of the fatigue damage area. It can be indirectly identified according to the vibration signal, which is the most widely used method now. For example, [PERSON] et al. [7] designed a three-roll rolling contact fatigue testing machine, which realizes fault diagnosis through the vibration and sound signals generated by the contact of the rolls. The time-domain characteristics of the vibration signal reflect the overall fatigue characteristics of the specimen, while the frequency analysis identifies the contact fatigue state of the specimen through Fast Fourier Transform (FFT). However, the superposition complexity of multiple high peaks in the vibration signal makes it difficult to identify the critical features of the specimen under different fatigue states. [PERSON] et al. [4] proposed a hybrid vibration signal analysis method, which uses FFT, Power Spectral Density (PSD) and waterfall methods to analyze vibration signals and evaluates the fatigue state of the specimen through windowed root mean square of digital weighting filters. However, the vibration signal features extracted by the windowed root mean square method are not sufficient, and may even lose important features, which requires the combination of time-frequency analysis methods [8; 9]. On the other hand, the segmentation of vibration signal levels lacks robustness to occasional noise impulses and non-stationary random disturbances.