ConditionSenseNet: A Deep Interpolatory ConvNet for Bearing Intelligent Diagnosis Under Variational Working Conditions

Abstract

Deep learning, with its ability of feature mining and logical judgement, has been widely studied in industrial intelligent diagnosis, including bearing fault diagnosis. However, an explicable and representable expression of deep learning architecture for the variational working conditions has been rarely discussed while it is known that vibration features from bearings are seriously influenced by variational working conditions. In this article, a deep interpolation ConvNet (DICN) architecture with three special layers, consisting of multiple sub-ConvNet units, weight unit, and fusion unit, is presented with the basic deep ConvNet architecture. Different from the traditional network, the first sub-ConvNet extracts the fault features under different working conditions, while the corresponding condition weight unit is learned from a working condition identification task. With the principle of interpolation theory, fusion unit is employed to achieve a sound fault feature representation under unknown working condition, which is named as ConditionSenseNet (CSN). This CSN architecture provides a way to dynamically express the crucial features hidden in the samples with the influence of working conditions suppressed, especially the variational working factors will be interpolated in this nonlinear fitting model. Additionally, three experimental studies are tested to verify the effectiveness of the proposed DICN method for bearing intelligent diagnosis under variational working conditions. The results and comparisons with other seven deep learning models show the proposed method shows outstanding robustness and higher accuracy where the accuracy of DICN is higher than the one of convolution neural network by more than 9% even if the working condition is variational.