Vision-based vibration measurement and compound defects feature extraction for rolling bearings based on multi-frequency phase

High-speed cameras have been widely adopted as a non-contact alternative to conventional contact sensors for mechanical condition monitoring. Traditional image spatial filtering faces two challenges: selecting a appropriate spatial frequency parameter, and phase wrapping caused by the arctangent function’s principal-value interval when encoding displacements from phase differences. To overcome these issues, a vision-based phase motion analysis method using a complex-valued steerable pyramid decomposition is presented for measuring bearing vibration signals. By constructing multiple sets of complex-valued filters to extract sequential image phase information at different spatial frequencies and orientations, and then jointly solving the phases, the problems of temporal wrapping and spatial discontinuity are resolved. To handle the unknown coupling relationships in compound faults of rolling bearings, a periodic-reconstruction-enhanced fast nonlinear blind deconvolution method is proposed. A periodic enhancement and reconstruction mechanism is incorporated into the fast nonlinear blind deconvolution algorithm to enhance impulse trains of each fault period, and then defect characteristic frequencies are obtained via envelope demodulation. For the two parameters in the blind deconvolution algorithm that are sensitive to the results, namely the filter length and the deconvolution period, the envelope autocorrelation function is used to analyze the impulse period. Then, under the determined deconvolution period, the optimal filter length is selected using the minimum multi-scale permutation entropy as the criterion. Through experiments, the inner race and outer race defect features were successfully separated and extracted from the vision-measured compound fault signal of the bearing. Additionally, the advantages of the proposed methods were fully validated through different illumination experiments and comparisons with various other methods. This work provides a solution for non-contact condition monitoring.