Nothing Special   »   [go: up one dir, main page]

Zhou et al., 2018 - Google Patents

An information fusion model based on Dempster–Shafer evidence theory for equipment diagnosis

Zhou et al., 2018

Document ID
1966072140432159547
Author
Zhou D
Wei T
Zhang H
Ma S
Wei F
Publication year
Publication venue
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

External Links

Snippet

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and …
Continue reading at asmedigitalcollection.asme.org (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024Quantitative 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • G05B23/0243Electric 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227Qualitative 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
    • G05B23/0229Qualitative 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks

Similar Documents

Publication Publication Date Title
Zhou et al. A new gas path fault diagnostic method of gas turbine based on support vector machine
Sarkar et al. Data-driven fault detection in aircraft engines with noisy sensor measurements
Li Gas turbine performance and health status estimation using adaptive gas path analysis
Ayodeji et al. Support vector ensemble for incipient fault diagnosis in nuclear plant components
Liu et al. Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy Petri nets
Shi et al. Remaining useful life prediction of bearings using ensemble learning: The impact of diversity in base learners and features
Sadough Vanini et al. Multiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks
Elwany et al. Real-time estimation of mean remaining life using sensor-based degradation models
Lee et al. A fault diagnosis method for industrial gas turbines using Bayesian data analysis
Wang et al. A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C‐Means Clustering and Support Vector Machine
Que et al. A data-driven health prognostics approach for steam turbines based on xgboost and dtw
Soleimani et al. Diagnostics and prognostics for complex systems: A review of methods and challenges
Zhou et al. An information fusion model based on Dempster–Shafer evidence theory for equipment diagnosis
Mazidi et al. A health condition model for wind turbine monitoring through neural networks and proportional hazard models
Losi et al. Prediction of gas turbine trip: A novel methodology based on random forest models
Feng et al. A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine
Borguet et al. Regression-based modeling of a fleet of gas turbine engines for performance trending
Correa-Jullian et al. Opportunities and data requirements for data-driven prognostics and health management in liquid hydrogen storage systems
Su et al. Failure prognosis of complex equipment with multistream deep recurrent neural network
Losi et al. Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data
Bechini et al. Statistical rule extraction for gas turbine trip prediction
Lin et al. A data-driven fault diagnosis method using modified health index and deep neural networks of a rolling bearing
Yan et al. Two‐Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion
Wang et al. Research on anomaly detection and positioning of marine nuclear power steam turbine unit based on isolated forest
Losi et al. Ensemble Learning Approach to the Prediction of Gas Turbine Trip