Key Takeaways & Executive Findings
- •• Proposes a novel performance-based FDI strategy using a first-order Takagi-Sugeno-Kang fuzzy inference system to handle measurement uncertainties in gas turbine engines. • Introduces a robust structure combining a specialized fuzzy inference system with the TSK-based FDI system, achieving 2%–8% improvement in success rate under large measurement bias conditions. • Utilizes parameter correction and power-level angle scheduling to reduce system complexity and rule count, enhancing computational efficiency. • Demonstrates superior accuracy in fault detection, isolation, and identification compared to existing methods, with desirable online performance.
Abstract
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis. While current research focuses mainly on measurement noise, measurement bias remains challenging. This study proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system. To handle ambient condition changes, we use parameter correction to preprocess the raw measurement data, which reduces the FDI’s system complexity. Additionally, the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system. The data for designing, training, and testing the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases. The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies. Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection, isolation, and identification. The robust structure demonstrates a 2%–8% improvement in the success rate index under relatively large measurement bias conditions, thereby indicating excellent robustness. Accuracy against significant bias values and computation time are also evaluated, suggesting that the proposed robust structure has desirable online performance. This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
1. Introduction
The gas turbine engine fault detection and identification (FDI) system is a crucial component of advanced engine control and health management systems. An effective FDI system ensures engine safety, reduces maintenance costs, and minimizes the risk of catastrophic failures [1]. Changes in gas-path parameters including speed, temperature, pressure, and flow rate are used in the FDI system to determine how the engine performance differs from the desired state. FDI techniques can be categorized into two: model-driven and data-driven [2].
The Kalman filter (KF) is the representative and the most popular model-based method owing to its numerous advantages, including high estimation accuracy for linear problems, low computational complexity, and the ability to handle sensor noise and biases effectively. It was first successfully applied to aircraft engine health estimation in the late 1980s [3–5], which encouraged the use of KF-based techniques in aircraft engine health management. Pratt and Whitney proposed a diagnostic system called the enhanced self-tuning onboard real-time model (eSTORM) based on a modified KF and applied it to a PW6000 engine [6]. In Refs. [7] and [8], an extended Kalman filter (EKF) and hybrid Kalman filter (HKF) were proposed to address the limitations of nonlinear gas path diagnostic problems. Many research results have been addressed from various perspectives [9–11]. The major drawbacks of the KF-based method include the following. (1) Even EKF-based methods can only handle problems with limited nonlinearity. Estimates for the nonlinear diagnostics problems are often biased and suboptimal [12]. (2) The performance of the KF-based method is easily affected by smearing, which means that this algorithm tends to attribute a single fault to several components when the number of measurements is limited [13].
Machine learning algorithms, which can deal with the nonlinearity of gas turbine engines and have high computational speed, have been widely used for gas turbine engine fault diagnosis in recent years. A commonly used diagnostic strategy involves modeling an engine and building a residual generator. The residuals generated ...
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Shuai Ma, Yafeng Wu, Zheng Hua, Linfeng Gou (2025). Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-024-01145-y
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines using a first-order Takagi-Sugeno-Kang fuzzy inference system, specifically designed to handle measurement uncertainties, including both noise and bias.
How does the proposed method handle measurement bias?
The method introduces a robust structure that combines a specialized fuzzy inference system with the TSK-based FDI system, which effectively mitigates the impact of measurement bias, achieving a 2%–8% improvement in success rate under relatively large bias conditions.
What optimization techniques are used in the TSK-based FDI system?
The antecedent parameters are optimized using particle swarm optimization, while the consequent parameters are optimized using ridge regression.
What are the key advantages of the proposed FDI strategy?
The proposed strategy offers superior accuracy in fault detection, isolation, and identification, reduced system complexity through parameter correction and scheduling, and desirable online performance with low computational time.
How was the proposed method validated?
The method was validated through comprehensive simulation studies using a component-level turbofan engine model, comparing its performance against existing methods and demonstrating its effectiveness under various measurement uncertainty scenarios.
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