Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms

Predicting the deterioration trend of bearings and determining the optimal replacement time is crucial for maintenance teams to effectively manage consumable parts, enhance equipment reliability, and prevent potential breakdowns. Given the common practice of periodic condition monitoring based on vibrations in large industries, utilizing this data to forecast future vibration trends is a key focus of this research. Considering the limited availability of data in industrial settings, it is essential for the model used to be capable of working with minimal data. To address these challenges, a run-to-failure test has been conducted on an industrial bearing sample, with its vibration data recorded. Acknowledging the impact of operational variations and uncertainties in vibration behavior even among identical bearings, leveraging the bearing’s vibration history for predictive modeling has been deemed critical. This study proceeds in three distinct phases: selecting the key characteristic for predicting its deterioration trend, identifying the feature to pinpoint the failure onset, and determining the most suitable model for forecasting future bearing vibration states. RMS has emerged as the optimal characteristic for trend prediction, while Peak and Kurtosis have been identified as effective indicators for failure onset detection. The selection of the deterioration estimation model has involved comparing the outcomes of SVR + Bootstrapping and RVR models under various limited data scenarios. Ultimately, the RVR method has proved superior, requiring just three data points for estimation and delivering results with a confidence level leading to an accuracy of approximately 94% for the analyzed bearing.