Fault detection for rolling bearings by multi-sensor information fusion method with adaptive weights

Driven by the increasing needs for production safety, a fault detection method based on multi-sensor fusion with adaptive weight coefficients is proposed in this paper to make full use of multi-measuring points information. To this end, considering the different information among multi-measuring points, the variance contribution rate (VCR) of vibration signals are used to design adaptive weight coefficients for data fusion to fully utilize the information contained in each vibration signal. On this basis, the least atoms contain time domain and frequency domain are extracted based on dictionary sparse representation (DSR) algorithm to represent the feature information of the original signal to weaken the influence of the curse of dimensionality. Finally, K-nearest neighbor distance is used in sparse residual space (SRS) for fault detection (K-SRS). The effectiveness of the proposed method is demonstrated by the rolling bearings data, and results show the advantage of our proposed approach.