Hybrid SOM-PCA method for modeling bearing faults detection and diagnosis

Rolling bearing faults always stay a thorny problem in the renewable energy field; therefore, the necessity of research and development in this area is essential. In this document, we propose a new approach in the faults detection and diagnosis of bearings during their operation. The principal aim of this study was to ensure a fast and efficient modeling of the unknown signals and their diagnostic, hence minimizing the damage in systems and the maintenance time and costs. The modeling step is based on the frequency's analysis of residues produced by (SOM-PCA) algorithm, and then, the diagnostic step is relied on the classification of the unknown signals in four operation cases possibly using the SOM model. Results indicate the efficiency of method to faults detection and diagnostic in experimental data such as the unsupervised classification of most faults.