A Lightweight Model for Bearing Fault Diagnosis Based on Gramian Angular Field and Coordinate Attention

Abstract

The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained increasing attention. A critical challenge is how to embed the characteristics of time series into DL to obtain stable features that correlate with equipment conditions. This study proposes a lightweight rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly, the time domain signal is encoded into GAF images after downsampling and segmentation. This method retains the temporal relation of the time series and provides valuable features for DL. Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features. After that, CA is employed to capture the long-range dependencies and identify the precise position information of the GAF images with nearly no additional computational overhead. The proposed method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the calculation overhead of the model and achieve high diagnostic accuracy.

A multi-layer feature fusion fault diagnosis method for train bearings under noise and variable load working conditions

“…The seven fault diagnosis approaches (CNN [47], MIRCNN [48], MMCNN [49], GAF_CA_CNN [50], SIRCNN [51], Bayes_DCGRU [52], and MCL_DRL [53]) are compared with the MFIRN. The capabilities of various approaches to diagnose faults under noise and variable load conditions are compared using two cases.…”

Prediction of brown tide algae using improved Gramian angular field and deep learning based on laser-induced fluorescence spectrum

“…GAF can translate a Cartesian coordinate system into a polar coordinate system, encoding 1D time series as 2D images. Additionally, the feature information of the original sequences is maintained [31]. The time series ′ value x is mapped to the radius, while the timestamp t is mapped to the angle.…”

“…Wang and Oates obtained high-precision classification by encoding a 1D sequence into various types of images using the process of integrating GAF and Markov transition field and connecting CNN to extract features [29]. Cui et al constructed a lightweight model for bearing defect identification using GAF in conjunction with an attention mechanism [31]. However, when GAF is used to convert a 1D spectrum, its internal normalizing approach will treat every spectrum equally, magnifying the distinctions between spectral classes and lowering the differences between varied concentrations of spectrum within the same species.…”

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

“…Fault features are crucial for bearing fault diagnosis. However, the failure of various components of the bearing leads to various characteristic frequencies [5][6][7][8].…”