Damage Identification of Vehicle Brake Disks by the use of Impedance-Based SHM and Unsupervised Machine Learning Method

Abstract

Although SHM (Structural Health Monitoring) has been widely used for aeronautical purposes, in the last decades new application scenarios have become applicable, such as the civil and automotive industries. Automotive components are increasing the maximum operational efficiency, aiming to obtain greater performance and safety of its mechanical systems at low production and maintenance costs. In this context, it is necessary to make predictive studies related to the incipient damages or about the useful life of the structures. The brake system represents one of the most important mechanical systems in a passenger vehicle since it deals directly with the preservation of their lives. Thus, in this contribution a regular vehicle brake disc is studied in order to evaluate the sensitivity of the impedance-based SHM application to identify mechanical changes and propose a method to check their integrities. With the purpose to promote structural changes, a virtual damage was created by mass addition with small magnets attached on the surface of the disc in different positions. Further, some experiments were conducted to have several state conditions of the brake discs (pristine and several virtually damaged cases). Then, the unsupervised machine learning technique called K-Means Clustering Method was applied to the data set and a quadratic regression model was used as well based on RMSD damage metric of the cases. Obtained results show the applicability of the method in the identification of damages, as well as the potential of the use of unsupervised machine learning methods and mathematical models in the context of SHM.