**6. Conclusions**

This paper proposed an automatic online methodology for monitoring ball bearings by optimizing the internal parameters of the OPTICS method and the dimension reduction step. The dynamic monitoring AOC-OPTICS was divided into three phases: the initialization, the detection and following the defect. The methodology was confronted with a simulated fault evolution and then with experimental data. The detection reached an accuracy of 100%. The follow-up was assured by geometrical values whose trend followed linear or exponential mathematical models with correlation coe fficients up to 0.994. This methodology brings many improvements: (I) This automated methodology used the best parameters for the detection and following the defects with high accuracy. (II) The variation of speed and load cannot lead to discovering the fault in the rolling bearing. Only the amplitude leads to detecting the faulty state. (III) The relief method is e fficient compared to chi-square, which is used to delete unnecessary features, which can make the iteration to be calculated speedily. (IV) The characteristics parameters related to the defect facilitate monitoring of the evolution with the times. (V) The density and Calinski and Davies–Bouldin index represent e fficacy more than the other parameters, for monitoring the defect growth trajectory. The major perspective is to add the diagnostic part in the methodology to increase the prognosis. This part must be based on previous knowledge provided by a digital twin or an expert.

**Author Contributions:** H.H. performed the experiments, conceived the algorithm and analyzed the data. X.C. and L.R. planned the experiments and supervised the research work. H.H. and X.C. wrote the paper and L.R. revised the entire paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
