Frequency-enhanced neural networks with a hybrid spall-size estimator for bearing fault diagnosis

Abstract

In the fault diagnosis of rolling element bearings (REBs), spall size is a typical indicator of fault severity. Conventionally, spall size estimation relies on expert-knowledge-based or data-driven approaches. Expert-knowledge-based approaches require accurate assumptions about spall-excited events, making them challenging to apply in field environments. In contrast, data-driven approaches often struggle with insufficient training data and limited generalization across various operating conditions. To address this challenge, this paper proposes a frequency-enhanced neural network (FENN) with a hybrid spall-size estimator (HSSE). The proposed FENN employs both one-dimensional convolution in the time domain and Fourier convolution on the frequency magnitude, while preserving phase information in the frequency domain to enhance frequency components that are associated with spall in REBs. The novel HSSE proposed here integrates a data-driven spall-size estimator and an expert-knowledge-guided spall-size estimator to capture spall entry and exit events between rolling elements and raceways. Model validation results, which analyzed both simulation and experimental data from roller and ball bearings, demonstrate that the proposed approach provides accurate predictions of spall size, even with limited training data. Additionally, it is confirmed that the proposed model identifies the mechanical frequencies associated with spall events, providing interpretable results from raw vibration signals without requiring further processing.