Bearing Life Cycle Prognostics via Unique Identifier Tracking
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Solution Overview
Problem
Current methods for predicting the service life of rolling-element bearings are inadequate due to variations in operational conditions and random fatigue processes, leading to unpredictable maintenance and potential failures.
Innovation Solution
A method that uses a unique machine-readable identifier to track the service life data of individual bearings throughout their life cycle, incorporating factors like rolling contact forces, lubrication conditions, and mechanical loads, and applies a specific mathematical life-cycle model to provide an accurate prognosis for residual life and maintenance planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional life prediction methods based on nominal operating conditions are used, then the prediction process is simple, but the prediction accuracy is insufficient due to variations in actual operation conditions and random fatigue processes
Solution Approach 1:
A unique machine-readable identifier is applied to the bearing at manufacturing, and a history log is created and stored in a database before the bearing enters service. This preliminary setup enables continuous tracking of service life data throughout the bearing's lifecycle, allowing for accurate prognosis without complex monitoring systems during operation.
Solution Approach 2:
Instead of physically monitoring the actual bearing in complex ways, the invention creates a digital copy (history log) of the bearing's service life data in a database. This digital replica tracks all relevant parameters and can be processed using mathematical models to generate accurate predictions without requiring complex physical measurement systems.
2Reliability
If comprehensive service life data is collected and accumulated for individual bearings, then accurate prognosis can be achieved, but data management and processing complexity increases
Solution Approach 1:
The history log in the database serves multiple functions: it stores service life data, tracks bearing identification, records operational parameters, and supports mathematical modeling for prognosis. This universal data structure handles all aspects of bearing lifecycle management in a single system, reducing overall complexity despite the comprehensiveness of data collection.
Solution Approach 2:
The database acts as an intermediary between the physical bearing and the prognosis system. Service life data is accumulated and stored in the database under the bearing's unique identifier, separating the data collection function from the analysis function. This intermediary structure simplifies data management by providing a centralized, organized repository that can be queried and processed as needed.
3Loss of time
If condition monitoring is implemented to detect signs of impending failure, then maintenance timing can be optimized, but the system cost and complexity increase
Solution Approach 1:
The system performs preliminary tracking of service life data from the moment of manufacturing through the bearing's entire lifecycle. By continuously accumulating data in the history log and applying mathematical life-cycle models, the system predicts residual life before failure occurs, enabling proactive maintenance planning without requiring complex real-time monitoring equipment.
Solution Approach 2:
The system uses feedback from accumulated service life data to continuously update the prognosis. The history log records actual operational conditions and performance, which are fed back into the mathematical models to refine predictions of residual life. This feedback mechanism enables accurate maintenance timing based on actual bearing condition rather than fixed schedules or complex real-time monitoring.
Data Source
AI summary
A life-cycle prognosis is created of a rolling-element bearing. As from manufacturing of the bearing and during the bearing's life-cycle that may include periods of the bearing's non-use, service life data is obtained indicative of one or more factors that occur during the life-cycle and that affect the length of the life-cycle. The service life data is obtained together with identification data that is representative of a machine-readable identifier, applied to the bearing at manufacturing. The identifier serves to uniquely identify the bearing throughout its life. The identification data is used for identifying in a database a history log of the bearing. The service life data is accumulated in the history log of the bearing as from the manufacturing. The history log is used with a specific mathematical life-cycle model for creating an update of the life-cycle prognosis.