Yang et al., 2018 - Google Patents
An IMM‐GLR Approach for Marine Gas Turbine Gas Path Fault DiagnosisYang et al., 2018
View PDF- Document ID
- 7552012632743241956
- Author
- Yang Q
- Li S
- Cao Y
- Publication year
- Publication venue
- Mathematical Problems in Engineering
External Links
Snippet
An IMM‐GLR approach based on interacting multiple model (IMM) and generalized likelihood ratio (GLR) estimation was developed to detect, isolate, and estimate gas turbine gas path fault (including abrupt fault and multiple faults) in the underdetermine estimation …
- 238000003745 diagnosis 0 title abstract description 38
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yildirim et al. | Aircraft gas turbine engine health monitoring system by real flight data | |
Marinai et al. | Prospects for aero gas-turbine diagnostics: a review | |
Kobayashi et al. | Application of a bank of Kalman filters for aircraft engine fault diagnostics | |
Vanini et al. | Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach | |
Yang et al. | A strong tracking filter based multiple model approach for gas turbine fault diagnosis | |
Simon et al. | Benchmarking gas path diagnostic methods: a public approach | |
Yang et al. | Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis | |
Simon et al. | Aircraft engine gas path diagnostic methods: public benchmarking results | |
Hale et al. | Design of built-in tests for active fault detection and isolation of discrete faults | |
Sun et al. | Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective | |
Yuan et al. | Hybrid diagnosis system for aeroengine sensor and actuator faults | |
Gou et al. | FDIA System for Sensors of the Aero‐Engine Control System Based on the Immune Fusion Kalman Filter | |
Ogaji et al. | Novel approach for improving power-plant availability using advanced engine diagnostics | |
Rausch et al. | Towards in-flight detection and accommodation of faults in aircraft engines | |
Yang et al. | An IMM‐GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis | |
Skliros et al. | Fault simulations and diagnostics for a Boeing 747 Auxiliary Power Unit | |
Fentaye et al. | Hybrid model-based and data-driven diagnostic algorithm for gas turbine engines | |
Ahsan et al. | Prognosis of gas turbine remaining useful life using particle filter approach | |
Wang et al. | Fault detection and diagnosis for gas turbines based on a kernelized information entropy model | |
Zhao et al. | Research on an adaptive threshold setting method for aero-engine fault detection based on KDE-EWMA | |
Samantaray et al. | Improvements to single-fault isolation using estimated parameters | |
Goebel et al. | Diagnostic information fusion: requirements flowdown and interface issues | |
Wu et al. | Fault detection and isolation of systems with slowly varying parameters—simulation with a simplified aircraft turbo engine model | |
Loboda | Gas turbine condition monitoring and diagnostics | |
Agarwal et al. | Model-based fault detection on modern automotive engines |