A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions

In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based on traditional machine learning and deep learning, have been developed and successfully applied on several benchmark datasets. However, in the real world, there are two major obstacles that prevent existing data-driven algorithms from being applied in actual industrial diagnostics applications: a) few-shot learning with limited labelled data, and b) high requirement for model's generalization ability to adapt different diagnosis circumstances. Two classic feature engineering methods of Order Tracking and Fast Fourier Transform give us inspirations to solve these problems. In this paper, we propose a data augmentation algorithm based on the core assumption of Order Tracking and present a self-adaptive convolutional neural network for fault diagnosis. The data augmentation algorithm utilizes resampling technique to simulate data under different rotating speeds and working loads, in which the Fast Fourier Transform is embedded alternately to calculate the frequency spectra of the expanded dataset. Based on the robust features in the spectra, the self-adaptive convolutional architecture is designed with much fewer Floating Points Operations (FLOPs) and trainable parameters than the deep counterparts, by which the extracted features are invariant for generalization and discriminative for classification. Experiments based on two bearing databases have been carried out and the results have verified the generalization ability and adaptability for few-shot learning of our proposed methods. (C) 2020 Elsevier Ltd. All rights reserved.