Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter

Abstract

Bearing fault diagnosis is vital for ensuring reliability and safety of high-speed trains and wind turbines. Therefore, a minimum unscented Kalman filter-aided deep belief network is proposed to extract invariant features from vibration signals collected by multiple sensors. This particularly crucial due to the dynamic nature of environmental noise and internal bearing degradation, which pose challenges to accurate diagnosis. Firstly, the Gramian angular summation field is employed to transform the multi-sensor signals into 2-D feature maps. This transformation retains the absolute temporal relation within the time-series signals, mitigating feature distortion and enhancing noise elimination for early detection. Secondly, a deep belief network is utilized to construct a robust deep learning framework capable of analysing the translated 2-D feature maps for effective diagnosis. In addition, a minimum unscented transform technique and an adaptive scaling process for noise are integrated into the diagnostic model. These components exhibit exceptional dynamic tracking capabilities, allowing for adjustment of key parameters in response to the prolonged and evolving bearing degradation process. The proposed methodology was rigorously evaluated through a comprehensive analysis involving nine distinct methods, utilizing two diverse bearing datasets. The results obtained underscore the superior attributes. Notably, the proposed diagnostic scheme achieved accuracy rates exceeding 98% and 99% for the two datasets, respectively. This achievement underscores the establishment of an intelligent diagnosis model characterized by high precision and exceptional generalisation capabilities for bearings within rotating machinery. Consequently, this work lays a robust foundation for future research endeavours, particularly in the realm of transfer learning.