Abstract
Bearing fault diagnosis has attracted increasing attention due to its importance in the health status of rotating machinery. The data-driven models based on deep learning (DL) have become more and more intelligent in the field of fault diagnosis, and among them convolutional neural network (CNN) has been widely used in recent researches. However, traditional CNN is not easy to capture right fault features due to their fixed geometric structures, especially under complex working conditions in fault diagnosis. To address these challenges, we propose a novel model by combining InceptionResnetV2 with Deformable Convolution Networks, named DeIN. We replace the basic form of convolution with deformable convolution in specific layers, and a main classifier and an auxiliary classifier are designed to output the classification result of our proposed model, to adapt to the non-rigid characters and larger receptive field in time-frequency graph (TFG). Experimentally, the one-dimensional signals are transformed into TFGs and as input of the proposed model, and this aims to find useful features during the training process. To verify the generalization ability of the proposed model, we apply a set of cross-over tests based on two popular datasets, and our model achieved 99.87% and 94.52% highest-precision fault classification results comparing with other state-of-the-art CNN models.
Highlights
As an indispensable component of rotating machinery, rolling bearings always determine the state of machines
DeIN can better adapt to the data of different working conditions with signal features and recognition ability, which method is similar to the attention mechanism and can find features that are valuable to the model during the network training process, and DeIN will be used in a wide range of PHM problems and solve fault diagnosis problems in more fields
In the bearing fault dataset, time-frequency graph (TFG) are the result of the STFT layer, at the same time, the offset field layers calculate the offset pn of the convolution kernel, which makes the convolution kernel obtains deformable receptive field, shown in Figure 4, and deformable convolution method expression of the convolution kernel by increasing pn could be expressed as Equation (11)
Summary
As an indispensable component of rotating machinery, rolling bearings always determine the state of machines. In order to achieve higher accuracy and better performance in bearing fault diagnosis under varying working conditions, we propose a novel DeIN model, which uses deformed convolution in low-dimensional features to extract useful signal features, and apply InceptionResNetV2 to process the output of deformed convolution. DeIN can better adapt to the data of different working conditions with signal features and recognition ability, which method is similar to the attention mechanism and can find features that are valuable to the model during the network training process, and DeIN will be used in a wide range of PHM problems and solve fault diagnosis problems in more fields. (3) The proposed DeIN, which reaches a high accuracy up to 99.87% and 94.52% on average under different working conditions and equipment This rest of the paper is organized as follows: Section II describes the deformable convolution and inception-resnet based fault diagnosis algorithm. DeIN’s efficient feature extraction capability is used to fit multiple fault data and improve model’s generalization
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