Jet Engine Temperature Sensor Signal Processing
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Solution Overview
Problem
Temperature sensors in jet engines exhibit inertia, leading to lag effects that cause malfunctions during rapid temperature variations, and existing correction techniques are complex and inaccurate due to the dispersion of sensor time constants and the need for additional estimators.
Innovation Solution
A method that processes temperature measurement signals by digital modeling and estimating a lag error signal, using a parameter K that varies based on the lag error to weight the measurement and modeled signals, allowing for accurate temperature signal generation even during transient phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed chart with average time constant values is used to correct measurement signals, then the correction process is simplified, but the accuracy deteriorates due to not accounting for sensor dispersion and individual sensor characteristics
Solution Approach 1:
The system uses the sensor's own measurement signal and a digital model of the sensor's inertia to estimate its individual time constant in real-time. This self-characterization approach eliminates the need for external charts or additional sensors, allowing each sensor to provide its own accurate correction parameters based on its actual characteristics.
Solution Approach 2:
The method implements a feedback mechanism where the estimated time constant is continuously refined using the sensor's measurement signal and the digital model. The lag error signal is calculated based on the difference between the measurement signal and the modeled signal, and this feedback is used to improve the time constant estimation and subsequent corrections.
2Measurement precision
If additional estimators are added to estimate flow rate and time constant, then the accuracy of correction is improved, but the device complexity increases
Solution Approach 1:
The sensor system performs its own time constant estimation using only its measurement signal and a digital model of its inertia. This eliminates the need for separate flow rate estimators or additional sensors, reducing system complexity while maintaining accuracy.
Solution Approach 2:
The digital model serves multiple functions: it represents the sensor's inertia characteristics, provides the basis for time constant estimation, and enables calculation of the lag error signal. This multi-functionality reduces the need for separate components and estimators.
3Adaptability or versatility
If the time constant is estimated in real-time using the measurement signal and digital model, then the adaptability to different sensors is improved, but the calculation complexity increases
Solution Approach 1:
Each sensor automatically characterizes itself by estimating its own time constant using its measurement signal and the digital model. This self-characterization provides adaptability to different sensor characteristics without requiring external intervention or complex manual calibration procedures.
Solution Approach 2:
The system uses a dynamic estimation approach where the time constant is calculated in real-time based on the current measurement signal and digital model. This dynamic adaptation allows the system to handle varying sensor characteristics and operating conditions effectively.
Data Source
AI summary
A method for processing a measurement signal T1 of a temperature delivered by a sensor includes: digitally modeling, by a modeled signal T2, the temperature measured by the sensor; and estimating a lag error signal for this sensor, based on the modeled signal T2 and of a signal T3 obtained by filtering the modeled signal, the filtering being parameterized by an estimate of a time constant of the sensor. A temperature signal is obtained by adding to a signal T4, derived from the measurement signal T1, the product of a real parameter K and a signal resulting from the subtraction of the signal T4 from the modeled signal T2. The value of the parameter K applied during the obtaining step varies over time and depends on the value of the estimated lag error signal.