Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging.

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods. • A reliable multi-scale decomposition method for CNN is proposed. • MSCNN extracts fault information over multiple scales with better feature extraction ability. • The proposed method yields best diagnostic ability by comparing with other five methods. [ABSTRACT FROM AUTHOR]