A three-dimensional vibration data compression method for rolling bearing condition monitoring

In condition monitoring for rolling bearings, it has achieved good diagnostic performance and clear mechanistic interpretation based on vibration data. The high sampling frequency of data collection preserves fault characteristics but brings the problem of big data. An effective way to reduce this problem is to apply data compression. However, in order not to affect the diagnostic performance of data, it is difficult to improve the compression ratio further. Inspired by the binarization method, the compression dimension of the bit cost of a single sample point is first introduced into the fault-mechanism-based method in this article. On this basis, a three-dimensional data compression method is proposed, and it is subsequently validated with two real-bearing datasets. Two performance metrics, including a newly defined one, are utilized to compare the proposed method with the five existing methods. The comparison results show that the proposed method significantly improves the compression ratio of data but maintains good diagnostic performance.This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1702400, in part by the Key Research and Development Program of Sichuan Province under Grant 23ZDYF0212, and in part by the China Scholarship Council with a Scholarship under Grant 202106070089