Rotating System Balancing Using Engine-Specific Transfer Functions
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Solution Overview
Problem
Conventional methods for balancing rotating systems in gas turbine engines are time-consuming and labor-intensive, requiring multiple iterations to achieve acceptable vibration levels due to challenges in predicting the mass and position of balancing masses.
Innovation Solution
A system utilizing machine-learned models to generate an optimized balance shot by analyzing various parameter values, including engine operating, assembly, usage, and environmental parameters, to determine the most effective locations for adding or removing balancing weights, thereby reducing vibration response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional balancing methods using generalized sensitivity factors are used, then the balancing process can be performed with standard procedures, but multiple iterations are required which increases time and labor intensity
Solution Approach 1:
The patent changes the parameters used in balancing from generalized sensitivity factors to engine-specific sensitivity factors that are tailored to each individual engine's characteristics. This allows the balancing process to account for unique engine properties, reducing the need for multiple iterations and significantly decreasing balancing time while maintaining ease of manufacture through systematic parameter adaptation.
Solution Approach 2:
The patent implements a feedback mechanism where vibration data from actual engine operation is collected and used to refine the sensitivity factors for that specific engine. This feedback loop enables continuous improvement of the balancing accuracy, allowing the system to converge to an optimal solution in fewer iterations compared to conventional methods that lack engine-specific feedback.
2Device complexity
If generalized sensitivity factors are used for predicting balancing mass and position, then the process can be simplified, but prediction accuracy is insufficient leading to multiple balance shots
Solution Approach 1:
The patent transforms the balancing approach by changing from universal sensitivity factors to engine-specific sensitivity factors. This parameter change maintains relatively simple process complexity while dramatically improving prediction accuracy, as the sensitivity factors are calibrated to each engine's unique characteristics, enabling more accurate prediction of balancing mass and position in a single shot.
Solution Approach 2:
The patent performs preliminary characterization of each engine to determine its specific sensitivity factors before the actual balancing operation. This preliminary action captures engine-specific properties that would otherwise require multiple iterations to discover, thereby improving prediction accuracy without significantly increasing overall process complexity.
3Reliability
If multiple iterations of balancing are performed to achieve acceptable vibration levels, then adequate balancing can be achieved, but the number of flight tests increases reducing engine time-on-wing
Solution Approach 1:
By changing from generalized to engine-specific sensitivity factors, the patent achieves adequate balancing reliability in a single iteration rather than requiring multiple balance shots. This eliminates repeated disassembly and assembly cycles, thereby maximizing engine time-on-wing and reducing the duration the engine is out of service.
Solution Approach 2:
The patent enables the engine to essentially balance itself by using its own operational vibration data to generate accurate sensitivity factors. This self-service approach achieves reliable balancing results without requiring multiple external intervention iterations, thus preserving engine availability and time-on-wing.
Data Source
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AI summary
A system (100) and method (300) for reducing the vibration response of a rotating system (112) are provided. In one aspect, an optimized balance shot (150) or solution that indicates one or more physical locations at which one or more balancing weights are to be added or removed from the rotating system (112) is generated. The balance shot (150) is generated based on a transfer function (180) that is customized specifically for the rotating system (112). The transfer function (180) is generated by applying one or more machine-learned models (132) to parameter values for parameters that are associated with the rotating system (112). The machine-learned models (132) can generate main effects plots (160), and from the plots (160), an effective set of parameter values (170) can be determined. The transfer function (180) can be generated using the effective set of parameter values (170) so that the transfer function (180) used to generate the balance shot (150) is optimized specifically for the rotating system (112) undergoing the balancing process.