Abstract
Rolling bearings play a significant role in rotating machinery. Due to the failure of these components, the operations of the whole machinery are compromised and get out of service, which ultimately causes significant workload overhead and monetary loss. Many techniques are proposed for the diagnosis of faults in the rolling bearing; these techniques are manual and require a large amount of time for identification and correction of faults, which is not suitable for routine maintenance and operability of this rotating machinery. Therefore, there is a need for promising techniques for autonomous and reliable fault diagnosis in rolling bearings. Furthermore, the proposed deep learning model for fault diagnosis is not able to provide efficient and reliable results and is vulnerable to degradation problems and lack of multi-scale feature extraction. This proposed model faces the issue of the degradation problem due to the disappearance of the gradient, which ultimately compromises the optimization process. To solve these issues above, the authors proposed a novel three-dimensional (3D) Kronecker convolution feature pyramid (KCFP), which efficiently inputs the acquired data without converting time-frequency domain and pixel loss. In our model, the single dilation rate is replaced by 3D Kronecker convolution, and 3D Feature Selection (3DFSC) is used for the local learning of features. The proposed research enhances feature representation and classification accuracy while mitigating model degradation. Authors evaluate the model on the Paderborn University and MFPT bearing datasets. KCFP achieves round (99.6%) classification accuracy, outperforming the MFF-DRN (98.6%) and standard CNN (97.5%) models for the Paderborn University dataset. KCFP achieves round (97.6%) classification accuracy, outperforming the MFF-DRN (97.0%) and standard CNN (95.7%) models for the MFPT dataset. These results demonstrate the potential of KCFP for reliable and efficient rolling bearing fault diagnosis in industrial applications.