[go: up one dir, main page]

Wang et al., 2011 - Google Patents

A Rough Set-based gas turbine fault classification approach using enhanced fault signatures

Wang et al., 2011

Document ID
7156473098506065529
Author
Wang L
Li Y
Abdul Ghafir M
Swingler A
Publication year
Publication venue
Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy

External Links

Snippet

Gas turbine engine health management has become more and more important because of its ability to optimize the total gas turbine operation. Gas path fault classification is one of the most important techniques in gas turbine engine health management. In this article, a Rough …
Continue reading at journals.sagepub.com (other versions)

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
    • G05B23/0218—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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • 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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition

Similar Documents

Publication Publication Date Title
Hanachi et al. Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey
Li Performance-analysis-based gas turbine diagnostics: A review
EP0315307B1 (en) Performance data processing system
Ntantis et al. Diagnostic Methods for an Aircraft Engine Performance.
Zedda et al. Gas turbine engine and sensor fault diagnosis using optimization techniques
Marinai et al. Prospects for aero gas-turbine diagnostics: a review
De Giorgi et al. A diagnostics tool for aero-engines health monitoring using machine learning technique
Ganguli Fuzzy logic intelligent system for gas turbine module and system fault isolation
Wang et al. A Rough Set-based gas turbine fault classification approach using enhanced fault signatures
Mast et al. Bayesian belief networks for fault identification in aircraft gas turbine engines
Li Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm
Fentaye et al. Hybrid model-based and data-driven diagnostic algorithm for gas turbine engines
Dewallef et al. Combining classification techniques with Kalman filters for aircraft engine diagnostics
De Giorgi et al. Development of a real time intelligent health monitoring platform for aero-engine
Alozie et al. An adaptive model-based framework for prognostics of gas path faults in aircraft gas turbine engines
Wang et al. Rough set diagnostic frameworks for gas turbine fault classification
JP6302755B2 (en) Data generation system for plant diagnosis
Osigwe et al. Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks
Sarkar et al. Symbolic transient time-series analysis for fault detection in aircraft gas turbine engines
Dogga et al. Explainable AI based remaining useful life estimation of aircraft engines
Eustace A real-world application of fuzzy logic and influence coefficients for gas turbine performance diagnostics
Sun et al. Bayesian network-based multiple sources information fusion mechanism for gas path analysis
Alexander et al. Gas turbine engine fault diagnostics using fuzzy concepts
Sampath Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm
Tao et al. Aircraft engine gas path fault diagnosis method based on gray AHP