Optimization automating monitoring based on classification for rolling bearing

This work presents an automated and optimized methodology for detection and tracking real-time rolling bearings defects. It contains three sequential loops. The first loop is the initialization, as well as the constitution of a class called healthy. It starts by extraction of features from the acquisition signals, then select the most correlated features to reduce the unimportant features by the Relief method, normalization data by z-score, and reduce the dimension by t-SNE method, ended with a calculation of OPTICS parameter’s Epsilon and Minpts. The second loop is the detection, which has the same initialization steps, besides, OPTICS for data classification. The third loop is the follow-up: this phase consists of projecting the matrix of defect features into the final base that has made it possible to detect the defect class and to track the evolution of this class over time. The proposed methodology has been validated numerically and experimentally for rolling bearing in the outer race

Early detection of bearing damage by means of decision trees

This paper presents a procedure for early detection of rolling bearing damages on the basis of vibration measurements. First, an envelope analysis is performed on bandpass filtered signals. For each frequency range, a feature indicator is defined as sum of spectral lines. These features are passed through a principal component model to generate a single variable, which allows tracking change in the bearing health. Thresholds and rules for early detection are learned thanks to decision trees. Experimental results demonstrate that this procedure enables early detection of bearing defects

Effect of vibration frequency and angle knee flexion on muscular activity and transmissibility function during static whole body vibration exercise

International audienc

Transmission of whole-body vibration to lower limb during dynamic squat exercise

International audienc