A new rolling bearing fault diagnoses method based on period-doubling bifurcation in the Hindmarsh–Rose model

Abstract

The Hindmarsh–Rose (HR) model is a three-dimensional oscillators susceptible to initial values, making it capable of amplifying even the slightest variations. On this basis, we proposed a rolling bearing fault identification method based on period-doubling bifurcation in the HR model and constructed a bearing fault experimental platform to validate our approach in this paper. Initially, we analyze the HR model’s bifurcation characteristics using the discrete mapping method to identify oscillators suitable for detecting bearing faults. We then select the multiplicative period bifurcation points of the HR model to differentiate between different types of bearing faults. Next, we decompose and reconstruct vibration signals using the Hilbert–Huang transform and calculate the amplitude characteristics of the fault frequency band as the input for the HR detection oscillator. Finally, bearing faults are identified based on the phase trajectory of period-doubling. Furthermore, a comparative analysis is conducted between the proposed methodology and the employment of the empirical wavelet transform. Our approach presents a new perspective for utilizing nonlinear oscillators in bearing fault diagnosis.