Fault detection and diagnosis in propulsion systems - A fault parameter estimation approach

Duyar, Ahmet; Eldem, Vasfi; Merrill, Walter C.; Guo, Ten-Huei

doi:10.2514/3.21165

Cited by 28 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Merrill (1985), Merrill, DeLaat, and Bruton (1988), and Merrill, DeLaat, and Abdelwahab (1991), the authors studied sensor failure detection for jet engines using a Kalman filter with a generalized likelihood ratio testing-based scheme. In Duyar and Merrill (1992) and Duyar, Eldem, Merrill, and Guo (1994) the authors derived linearized models of jet engine systems via the a-canonical form parameterization identification method and applied a parameter estimation approach in fault detection and isolation (FDI) for the space shuttle main engine. In Patton andChen (1992, 1997) and Patton, Chen, and Zhang (1997), the authors studied fault detection of jet engine sensor systems using an eigenstructure assignment technique to design observer-based residual generators, and they also studied its robustness.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for a turbine engine

Diao

Passino

2000

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334)

View full text Add to dashboard Cite

Fault detection and diagnosis for jet engines is complicated by the presence of engine-to-engine manufacturing differences and engine deterioration during normal operation, the complexity of an accurate engine model, and our inability to directly measure certain engine variables. Here, we work with a sophisticated component level model (CLM) simulation of a turbine engine (the General Electric XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of faults. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by a Levenberg-Marquardt method to train a Takagi-Sugeno fuzzy system to represent the engine. The resulting nonlinear model provides a reasonably accurate representation of manufacturing differences, engine deterioration, and fault effects. We use multiple copies of this nonlinear model, each representing a different fault, to generate error residuals by comparing them to the engine output. In fact, we manage the composition of the set of models with a ''supervisor'' that ensures that the appropriate models are on-line, and processes the error residuals to detect and identify faults. Robustness and fault sensitivity of the proposed approach are studied in the paper and the component model level simulation of the XTE46 engine is used to illustrate the effectiveness of the fault diagnosis scheme. r

show abstract

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for a turbine engine

Diao

Passino

2000

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334)

View full text Add to dashboard Cite

show abstract

“…In [3]- [5] the authors studied sensor failure detection for jet engines using a Kalman filter with a generalized likelihood ratio testing based scheme. In [6], [7] the authors derived linearized models of jet engine systems via the -canonical form parameterization identification method and applied a parameter estimation approach in fault detection and isolation for the space shuttle main engine. In [8]- [10] the authors studied fault detection of jet engine sensor systems using an eigenstructure assignment technique to design observer based residual generators, and they also studied its robustness.…”

mentioning

confidence: 99%

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Diao

Passino

2001

IEEE Trans. Contr. Syst. Technol.

149

View full text Add to dashboard Cite

Abstract-Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its on-line learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by "component level model" simulation of the General Electric XTE46 turbine engine.

show abstract

“…Assuming that a fault has already been detected, this approach processed Kalman filter equations iteratively for each of the root causes under consideration and subsequently ranked fault candidates in order of likelihood based on the estimation error norms. The application of a bank of estimators for fault detection and isolation was conducted by Merrill et al [5], Duyar et al [6], and Menke et al [7]. This approach uses multiple estimators, each of which is designed for detecting a specific fault.…”

Section: Introductionmentioning

confidence: 99%

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

Kobayashi

Simon

2003

Volume 1: Turbo Expo 2003

155

102

View full text Add to dashboard Cite

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

show abstract

Fault detection and diagnosis in propulsion systems - A fault parameter estimation approach

Abstract: The paper presents the development of a fault detection and diagnosis (FDD) system with applications to the Space Shuttle main engine. The FDD utilizes a model-based method with real-time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Cited by 28 publications

References 6 publications

Fault diagnosis for a turbine engine

Fault diagnosis for a turbine engine

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

Contact Info

Product

Resources

About