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The traditional acoustic-based diagnosis (ABD) technique based on single-channel testing has a significant engineering value. Since its diagnosis robustness is sensitive to sound signal acquisition location, it develops slowly. To solve this problem, the 2-dimensional (2D) sound field variation near the machine is adopted for diagnosis by the near-field acoustic holography (NAH)- based fault diagnosis method with array measurement. However, its performance is limited due to the neglect of the sound field normal change information. To dig the sound field fault information further, a 2.5-dimensional (2.5D) acoustic field diagnosis method is presented in this paper and its performance compared with the 2D technology is verified by the bearing diagnostic test. Different from the 2D technique with only one source image, the 2.5D acoustic field model consists of source image, holographic sound image, and the differences between them, and its effective feature model is constructed by Gabor wavelet feature extraction and random forest feature reduction algorithm. The diagnostic effect of the 2.5D technique compared with the 2D technique increases more than 11% in the bearing diagnostic test. It provides new ideas for the development of the NAH-based fault diagnosis method, and further improves the ABD technique-based array measurement.

Bearing Fault Diagnosis Based on Spatial Features of 2.5 Dimensional Sound Field