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The gearbox is one of the most important parts of mechanical equipment and plays a significant role in many industrial applications. A fault diagnostic of rotating machinery has attracted attention for its significance in preventing catastrophic accidents and beneficially guaranteeing sufficient maintenance. In recent years, fault diagnosis has developed in the direction of multidisciplinary integration. This work addresses a fault diagnosis method based on an image processing method for a gearbox, which overcomes the limitations of manual feature selection. Differing from the analysis method in a one-dimensional space, the computing method in the field of image processing in a 2-dimensional space is applied to accomplish autoextraction and fault diagnosis of a gearbox. The image-processing-based diagnostic flow consists of the following steps: first, the vibration signal after noise reduction by wavelet denoising and signal demodulation by Hilbert transform is transformed into an image by bispectrum analysis. Then, speeded up robustness feature (SURF) is applied to automatically extract the image feature points of the bispectrum contour map, and the feature dimension is reduced by principal component analysis (PCA). Finally, an extreme learning machine (ELM) is introduced to identify the fault types of the gearbox. From the experimental results, the proposed method appears to be able to accurately diagnose and identify different types of faults of the gearbox

An Approach to Fault Diagnosis for Gearbox Based on Image Processing