Rolling Bearing Life Prediction Using Peeling Length Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting the remaining life of bearings do not adequately consider the operating state of mechanical equipment, leading to inaccurate predictions.
Innovation Solution
A method and device for predicting the remaining life of rolling bearings in rotary machines by calculating an estimated peeling length and considering the operation status, involving steps to determine the maximum rolling element load, maximum contact surface pressure, and estimated peeling length based on actual data and operation status prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bearing life prediction methods are used, then the calculation process is simple, but the prediction accuracy is insufficient because operating state is not considered
Solution Approach 1:
The method performs preliminary calculation of the estimated peeling length based on bearing structure parameters and material properties before considering operating conditions. This preliminary result is then refined by incorporating actual operating state data, allowing the system to build upon pre-computed values rather than calculating everything from scratch, thus balancing accuracy with computational efficiency
Solution Approach 2:
The method transitions from static bearing life prediction to dynamic prediction by continuously incorporating actual operating state data (load, speed, temperature) into the calculation. The estimated peeling length is dynamically adjusted based on real-time operating conditions, making the prediction adaptive to changing operational environments while maintaining reasonable computational complexity through modular calculation steps
2Reliability
If operating state data is incorporated into the prediction, then the remaining life prediction becomes more accurate, but the data processing requirements increase
Solution Approach 1:
The method extracts only the essential operating state parameters (maximum rolling element load, peeling length) needed for accurate prediction from the overall operating data set. By focusing on these critical parameters rather than processing all available operational data, the system achieves high prediction reliability while minimizing data processing requirements
Solution Approach 2:
The method applies different levels of data processing to different aspects of the prediction problem. The estimated peeling length calculation uses detailed local information about bearing contact stresses and material properties at critical locations, while the overall life prediction integrates this with broader operating state data, optimizing the balance between data processing effort and prediction accuracy
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
A remaining life of a bearing is appropriately predicted. A bearing remaining life diagnosis method for predicting a remaining life of a rolling bearing used in a rotary machine and having inner and outer bearing rings and rolling elements between both the bearing rings includes a step of calculating a latest estimated peeling length, and a step of calculating a latest remaining life of the bearing based on a latest peeling length of the bearing ring. The step of calculating the latest estimated peeling length includes a step of calculating a maximum rolling element load, a step of calculating a maximum contact surface pressure from the maximum rolling element load, and a step of calculating the latest estimated peeling length of the bearing ring from the maximum contact surface pressure of the bearing ring.