Investigation of vibration dynamics and failure prediction of ball bearing in two rotor system: Qualitative and Quantitative approach

Abstract The study investigates the vibration dynamics and failure prediction of ball bearings under common failure scenarios. The experiments involve normal bearings, faulty bearings, and conditions involving external rubbing. Various analyses such as time series analysis, Fast Fourier Transform, recurrence analysis, and the Dragon-King test are employed for the classification of different vibrations created in the system. These analyses aim to assess the condition of faulty bearings and detect any anomalies or extreme events. The complementary cumulative distribution function (CCDF) proves to be an effective method for identifying outliers in temporal data, providing the probability of events in the time series. The CCDF plot is specifically utilized to reveal the probability of a dragon-king extreme event in the case of rubbing. Extracting essential recurrence metrics from Recurrence Plots, such as recurrence rate, mean recurrence time, recurrence time entropy, laminarity, and determinism, allows for a quantitative understanding of the system's behavior. The study contributes valuable insights into the vibrational characteristics of ball bearings and offers a robust methodology for predicting failures and identifying abnormal events in complex mechanical systems.