查看文章

Deep learning models have been widely studied in fault diagnosis recently. A mainstream application is to recognize patterns in spectrograms of faults. However, some common drawbacks still remain as following: a) Preprocess to improve the quality of spectrograms is rarely explored; b) Computing cost of a conventional CNN far exceeds the requirements of fast analysis in industry; c) Adequate labeled data cannot be acquired to train a comprehensive diagnosis model for varying working conditions. In this paper, an Adaptive Logarithm Normalization (ALN) is proposed to realize preprocess considering data distribution, it attempts to improve the quality of spectrograms via eliminating truncation phenomenon and enriching details simultaneously; Meanwhile, simplified lightweight models are built on the basis of present lightweight building blocks to reduce parameters, while maintaining high performances …