Novel morphological scale difference filter with application in localization diagnosis of outer raceway defect in rolling bearings

Abstract

The location dimension of a bearing outer raceway defect plays an important guiding role in failure prediction and other aspects. Therefore, an improved dynamic model of bearings is proposed. The advantage of the model is that it can accurately simulate the impact characteristics of defective bearings and improve computational efficiency. Then, the relationship between the horizontal–vertical synchronized root mean square and the defect location dimension in different conditions is mapped using the model. Subsequently, a new index, named morphology horizontal–vertical synchronized root mean square (MHVS-RMS), is constructed based on the approximate analytical solution of the dynamic model of bearings and the mathematical principle of morphology. The function relationship between MHVS-RMS and the location dimension, as well as its principle of noise reduction and feature enhancement, are derived and proven. Finally, in view of the problem that noise interference in actual signals affects the accuracy of localization diagnosis, a new morphological filter and feature enhancement algorithm, named morphological scale-difference filter, is proposed via the above principles. The performance analysis results demonstrate that the proposed method can remarkably improve diagnosis accuracy.