Efficiency and Accuracy Unveiled: The Image-Based Life Model for Roller Bearing Vibration Prognosis

Abstract

The image-based life model is a method used to analyze the intelligent maintenance and safe operation of industrial roller bearings. However, due to variable operating conditions, the prognosis of bearing vibrations is often complicated by the variable operating conditions of real industry. These characteristics reflect the deterioration trends of the bearings, making the development of life prediction models exceptionally challenging. This manuscript presents a solution to address these difficulties by proposing the concept of an image driven life prediction model. It leverages lifespan data from roller bearings, encompassing regular operational phases to malfunction. An image state matrix designed to differentiate between various operational states of roller bearings. Historical bearing examination data from the University of Cincinnati Laboratory Center are employed to construct a life probability density function. The variables integrated into the state matrix model are dynamically adapted to enable real-time monitoring of bearing conditions in industrial applications. This work not only provides theoretical insights but also highlights the inadequacies of threshold limits in the context of the big data era. Long-range prediction can be enhanced by fusion of image-based state matrix model with traditional models for fault detection and diagnosis. Visual information through image help to train the model with real operating conditions of industrial bearing.