Epicyclic Gear Train Monitoring via Phase-Based Wave Resampling
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Solution Overview
Problem
Existing methods for monitoring mechanical transmission devices in aircraft, particularly epicyclic gear trains, are inadequate in detecting anomalies effectively due to their complex arrangement of toothed wheels.
Innovation Solution
A method involving the acquisition of primary mechanical wave data and rotation data from an epicyclic gear train, followed by calculating secondary rotation data to simulate resampling based on the phase of contact points between toothed wheels, allowing for the detection of anomalies using Fourier transforms and power spectral density analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic sensor measurement is used to monitor mechanical transmission device, then anomaly detection capability is provided, but measurement precision is insufficient for complex epicyclic gear trains
Solution Approach 1:
The patent segments the monitoring process into distinct phases: acquiring primary mechanical wave data at fixed time intervals, calculating contact point positions and phases, resampling the data at constant phase intervals, and performing spectral analysis. This segmentation transforms the complex continuous monitoring problem into manageable discrete steps, improving measurement precision for epicyclic gear trains.
Solution Approach 2:
The patent transitions from time-domain sampling to phase-domain sampling by introducing a new dimension (phase angle) for data organization. Instead of analyzing mechanical wave data only at fixed time intervals, the invention resamples data at constant phase intervals of contact points, enabling precise anomaly detection that accounts for the periodic nature of gear meshing.
2Ease of manufacture
If fixed time interval sampling is used for mechanical wave data, then data acquisition is simplified, but anomaly detection accuracy deteriorates due to variable rotation speeds
Solution Approach 1:
The patent performs preliminary calculations of contact point positions and phases before resampling the mechanical wave data. By pre-calculating the phase information based on rotation speeds and gear geometry, the system prepares the necessary transformation data in advance, enabling accurate phase-based resampling that compensates for variable rotation speeds.
Solution Approach 2:
The patent changes the sampling parameter from fixed time intervals to constant phase intervals. This parameter transformation adapts the sampling rate dynamically to the rotation speed variations, ensuring that each phase of the gear meshing cycle is sampled at consistent intervals, thereby maintaining anomaly detection accuracy across varying operating conditions.
3Measurement precision
If phase-based resampling is implemented, then anomaly detection precision is improved, but calculation complexity increases
Solution Approach 1:
The patent replaces complex mechanical anomaly detection methods with signal processing and mathematical transformations. By using Fourier transforms and power spectral density analysis on phase-resampled data, the invention substitutes physical inspection complexity with computational analysis, achieving higher precision while managing calculation complexity through efficient algorithms.
4Productivity
If conventional monitoring methods are used, then device simplicity is maintained, but productivity in terms of maintenance efficiency is reduced
Solution Approach 1:
The patent implements feedback through continuous monitoring and analysis of mechanical wave data, comparing spectral characteristics against baseline values to detect anomalies. This feedback mechanism enables proactive maintenance by identifying issues before they lead to failures, significantly improving maintenance efficiency despite the increased complexity of the monitoring system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient anomaly detection in epicyclic gear trains by transforming mechanical wave data into a phase-based sampling method, effectively locating mechanical anomalies and facilitating early wear detection, thus optimizing maintenance schedules.
Implementation Method 1
The acoustic sensor measures an acoustic signal, generated by mechanical vibrations in the mechanical transmission device
Implementation Method 2
measuring, at a plurality of successive time instants, values of a speed of rotation... An anomaly in the operation of the mechanical transmission device results in characteristic peaks in the frequency spectrum of the acoustic signal measured
Data Source
AI summary
A method for monitoring an epicyclic gear train of an aircraft includes the following steps: acquiring, at a predetermined sampling frequency, first values (5(ti)) of a signal formed by a progressive mechanical wave generated in the epicyclic gear train; measuring, at a plurality of successive instants, values (Vmes _r(tj)) of a speed of rotation of at least one of the toothed wheels of the gear train; calculating values (Vc(tj)) of a speed of rotation of a point of contact between two toothed wheels of the epicyclic gear train; determining second values (S(ç½)) of the signal formed by a progressive mechanical wave generated in the epicyclic gear train, the second values being sampled depending on a phase of the point of contact and forming secondary mechanical wave data; and using the secondary mechanical wave data (S(ç½)) to detect an anomaly related to the operation of the epicyclic gear train.