Aero-Engine Sensor and Actuator Fault Estimation Using LFT
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Solution Overview
Problem
Existing fault diagnosis technologies for aero-engine sensors and actuators cannot accurately estimate fault signals under external disturbances and modeling errors, lacking robustness and effective maintenance recommendations.
Innovation Solution
A method based on Linear Fractional Transformation (LFT) for fault diagnosis of aero-engine sensors and actuators, which establishes an affine parameter-dependent LPV model, converts it into an LFT structure, and designs an H∞ synthesis framework for a fault estimator using Linear Matrix Inequalities (LMIs) to adaptively adjust parameters and accurately reconstruct fault signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If observer-based fault estimation methods are used for LPV systems, then the fault estimation can be implemented, but the system becomes sensitive to external perturbations and model uncertainty
Solution Approach 1:
The patent introduces an H∞ filter as an intermediary component between the system output and fault estimation. This filter acts as a mediator that processes the system measurements while actively rejecting the effects of external disturbances and model uncertainties, thereby protecting the fault estimation from these harmful factors.
Solution Approach 2:
The patent transforms the fault estimation problem by changing the parameterization approach - using H∞ norm optimization instead of traditional observer-based methods. This parameter change in the estimation methodology allows the system to achieve both accuracy and robustness by optimizing the filter parameters to minimize the worst-case effect of uncertainties.
2Reliability
If H∞ optimization technology is used for fault estimation, then the robustness of the system is improved, but the research is still in the beginning stage and many issues need further discussion
Solution Approach 1:
The patent segments the fault estimation framework into distinct modular components: the H∞ filter design module, the LPV system model module, and the fault reconstruction module. This segmentation allows each component to be designed and optimized independently, reducing the overall complexity while maintaining robustness.
Solution Approach 2:
The patent performs preliminary actions by pre-defining the H∞ filter structure and optimization criteria before implementing the fault estimation. By establishing the robust framework in advance with predefined performance requirements, the system avoids complex adaptive adjustments during operation, simplifying the overall implementation.
3Ease of manufacture
If only fault detection is researched without fault signal estimation, then the implementation is simple, but accurate judgment of sensor and actuator states and maintenance cost reduction cannot be achieved
Solution Approach 1:
The patent extends the fault diagnosis process from discrete detection events to continuous fault signal estimation. The H∞ filter continuously processes system measurements to reconstruct fault signals in real-time, providing ongoing information about fault severity and evolution, which enables continuous monitoring and informed maintenance decisions.
Solution Approach 2:
The patent replaces the simple binary detection mechanism with a sophisticated signal processing system. Instead of merely detecting whether a fault exists, the system uses H∞ optimization-based signal reconstruction to estimate the actual fault signal, substituting a more advanced information processing approach that extracts meaningful quantitative data.
Data Source
AI summary
The present invention discloses a method for fault diagnosis of the sensors and actuators of an aero-engine based on LFT, and belongs to the field of fault diagnosis of aero-engines. The method comprises: establishing an aero-engine state space model using a combination of a small perturbation method and a linear fitting method; establishing an affine parameter-dependent linear-parameter-varying (LPV) model of the aero-engine based on the model; converting the LPV model of the aero-engine having perturbation signals and sensor and actuator fault signals into a linear fractional transformation (LFT) structure to obtain an synthesis framework of an LPV fault estimator; solving a set of linear matrix inequalities (LMIs) to obtain the solution conditions of the fault estimator; and designing the fault estimator in combination with the LFT structure to realize fault diagnosis of the sensors and actuators of an aero-engine.