An Experimental Setup to Detect the Crack Fault of Asymmetric Rotors Based on a Deep Learning Method

Abstract

Crack is a common fault of rotor systems. The research on crack fault detection methods is mainly divided into numerical and experimental studies. In numerical research, the current fault detection algorithms based on deep learning are mostly applied to bearings and gearboxes, and there are few studies on rotor fault diagnosis. In experimental research, the rotors used in an experiment are mostly single-span rotors. However, there are complex structures such as multi-span rotor systems in the actual industrial field. Thus, the fault detection algorithms that have been successfully applied on single-span rotors have not been verified on complex rotor systems. To obtain a fault signal close to the actual asymmetric shaft system of an asymmetric rotor system and validate the fault detection method, the crack fault detection platform is designed and built independently. We measure the vibration signals of three channels under five working conditions and establish an intelligent detection method for crack location based on a residual network. The factors that influence fault detection performance are analyzed, and the influence laws are discussed. Results show that the accuracy and anti-noise performance of the proposed method are higher than those of the commonly used machine learning. The average accuracy is 100% when SNR (signal-to-noise ratio) is greater than or equal to −2 dB, and the average accuracy is 98.2% when SNR is −4 dB.