Abstract: The vibration-based feature extraction is the main approach for mechanical fault diagnosis, whereas, in some conditions vibration signal is not easily measured because of its contact-measuring. Acoustic-based diagnosis(ABD) can overcome this disadvantage. However, for traditional ABD it is hard to choose proper measuring positions and the acoustic signals acquired based on single channel measurement can be used only for local analysis. Based on near-field acoustic holography(NAH), a new feature extraction method by using sound field distribution for rolling bearing fault diagnosis is presented. Firstly, sound fields in different bearing conditions are reconstructed by NAH. Using gray level co-occurrence matrix(GLCM) features extracted from acoustic images, the inner relationship between bearing conditions and sound fields is established. These features are fed into support vector machine(SVM) classifier for fault diagnosis. The effectiveness of our proposed method is demonstrated on the experimental investigation. The method provides a new reference for mechanical fault diagnosis.

Key words: Fault diagnosis, Feature extraction, Gray level co-occurrence matrix, Near-field acoustic holography, Rolling bearing, Support vector machine