Exploring a Cutting-Edge Framework for Bearing Fault Detection: A Synergistic Approach Integrating Statistical Analysis and Deep Learning Methods

Abstract

Bearing condition monitoring in the field of industrial machinery has increasingly relied on the incorporation of artificial intelligence techniques. This article introduces a fault detection and diagnosis methodology for bearing condition monitoring processes, utilizing the Mahalanobis squared distance (MSD). In the initial phase, a health index, namely MSD, is proposed to accurately indicate the health condition of the spherical bearing in an induction motor based on vibration signals. The MSD serves as a preclassification stage, effectively addressing the issue of data overlap and facilitating the identification of distinct data classes, particularly in cases where nonlinear and non-Gaussian data are prevalent. In the subsequent phase, a deep learning (DL)-based approach utilizing transfer learning is employed for the classification of the labeled dataset by MSD. Three established models, namely AlexNet, VGG19, and ResNet50, pretrained on the ImageNet dataset, are considered. These models are further fine-tuned using scalogram images generated through the application of continuous wavelet transform on the vibration signals obtained from spherical roller bearings. This integrated approach for fault detection and diagnosis is presented and validated using the intelligent maintenance systems bearing dataset. The results obtained demonstrate the reliability and efficacy of the proposed approach in accurately detecting and diagnosing bearing faults. Furthermore, the experimental findings indicate that the proposed approach surpasses existing state-of-the-art methods documented in the relevant literature.