Rolling bearing fault diagnosis method based on data-driven random fuzzy evidence acquisition and Dempster–Shafer evidence theory

Rolling bearing is of great importance in rotating machinery, so the fault diagnosis of rolling bearing is essential to ensure safe operations. The traditional diagnosis approach based on characteristic frequency was shown to be not consistent with experimental data in some cases. Furthermore, two data sets measured under the same circumstance gave different characteristic frequency results, and the harmonic frequency was not linearly proportional to the fundamental frequency. These indicate that existing fault diagnosis is inaccurate and not reliable. This work introduced a new method based on data-driven random fuzzy evidence acquisition and Dempster–Shafer evidence theory, which first compared fault sample data with fuzzy expert system, followed by the determination of random likelihood value and finally obtained diagnosis conclusion based on the data fusion rule. This method was proved to have high accuracy and reliability with a good agreement with experimental data, thus providing a new theoretical approach to fuzzy information processing in complicated numerically controlled equipments.