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Film cooling is an effective technology for improving the thrust-to-weight ratio of an aero-engine. The axial orientation of film cooling holes is one of the key factors determining the film cooling effect. However, the machining quality of cooling holes is still carried out by manual inspection, whose accuracy and consistency cannot be guaranteed. To address this problem, this paper proposes an axial orientation extraction algorithm based on laser point cloud processing. Firstly, the blade surface is scanned by a line laser sensor and a blade point cloud is obtained. Secondly, a single film hole can be segmented from the blade point cloud based on scanning line fitting residual. Thirdly, an improved Gaussian map is to complement the arc belt obtained by Gaussian map to obtain a complete circle along an error-sensitive direction. Finally, the axial orientation is estimated by random sample consensus with an initial value. The accuracy and consistency of the algorithm are verified by comparing the extraction result by the algorithm with those by the Geomagic software. The result shows that the axial accuracy extracted by the algorithm can reach 0.405°. [ABSTRACT FROM AUTHOR]

Rolling bearings typically operate under time-varying conditions, which present challenges for fault diagnosis due to the presence of modulation effect and noise component in the bearing vibration signal. This paper introduces a novel feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD), Teager-Kaiser energy operator (TKEO), and self-organizing map (SOM) to enhance the identification accuracy of bearing characteristics by removing unwanted components from the vibration signal. Firstly, CEEMD and TKEO are used to generate demodulated instantaneous energies of the raw vibration signal and each decomposed mode, which represent bearing features. Next, relevant features are extracted by computing similarity distances using the SOM network. The feature signal is then reconstructed, and its power spectrum is used to evaluate the visualization of the characteristic frequencies of rolling bearings. The efficiency of the proposed method is confirmed through the analysis of simulated and experimental vibration signals collected from two types of bearings with different geometries. The results demonstrate that the proposed method can effectively suppress noise, extract characteristics, and identify the fault condition of rolling bearings compared to previous studies. [ABSTRACT FROM AUTHOR]