Severity Estimation of Faulty Bearings Based on Strain Signals From Physical Models and FBG Measurements

Abstract

Condition based maintenance (CBM) is the preferred approach in rotating machinery and aim to replace the commonly used approach of maintenance based on service time. To achieve an effective CBM, different types of sensors should be placed in the system for condition monitoring to detect the location of the fault and its severity. In this research, a Fiber Bragg Grating (FBG) has been used for condition monitoring on spalls in deep grove ball bearings. The motivation for using these sensors is the ability to get a high-noise signal (SNR) ratio. The usage of FBG sensors is relatively new for health monitoring systems of rotating machinery. Therefore, there is not enough understanding of the strain signature measured by the FBG. To examine the phenomena in the strain signals, a physics-based model of the strain signature has been developed. In this model, two complementary models were integrated, a finite element (FE) model and a dynamic model . The strain model describes the interaction between the rolling elements (REs) and the bearing housing and simulates the strain behavior measured on the bearing housing. The simulation results are validated with strain signals measured by the FBG sensor at different stages of an endurance test. The model allows simulation of a wide range of spall lengths and describes the behavior of the strain signals for different levels of misalignment. The insights from the model enabled the development of an automatic algorithm that assess the severity of the defect and to track spall length during bearing operation, based on strain signals.