The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components.

Contact pattern measurement is commonly applied to assess gear transmission and meshing performance in aviation spiral bevel gear (ASBG) manufacturing enterprises. However, these patterns cannot be directly segmented and obtained from point clouds or images because each tooth of the spiral bevel gear has complex 3D spiral surface and texture information. Moreover, research on the high-precision 3D colour reconstruction of spiral gear tooth surfaces is relatively rare. Aimed at these problems, the current work proposed a high-precision 3D colour reconstruction method of spiral bevel gear. This method is based on the fusion of the image and point-cloud information. It includes two core steps to satisfy the practical demands of high precision and high efficiency. First, dense point clouds of the 3D calibration block are converted to a plane using the bird’s eye view method for corner detection. Second, the corners are quickly and accurately detected using a two-stage corner detection algorithm based on the inherent geometric features of the calibration block. The identified corners are then used to solve the transformation matrix from the pixel coordinate system to the line laser coordinate system. Image textures can be precisely projected and fused onto a 3D geometry for the coloured reconstruction of the gear tooth surface on the basis of this transformation matrix. The proposed method was validated by applying it to the 3D colour reconstruction of tooth surfaces of 10 distinct types of ASBGs. The experimental results demonstrated that the re-projection error of the proposed method is less than 0.1 millimetres. The minimal margin of error enables high-precision 3D colour reconstruction of the spiral bevel gear tooth surface. Compared to other classic 3D colour reconstruction methods, the proposed method achieves superior accuracy and efficiency. An experimental device was also developed for the 3D colour reconstruction of the tooth surface of ASBGs, as illustrated in figure 1.