Li et al., 2001 - Google Patents

Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems

Li et al., 2001

View PDF
Document ID
14784275959949558589
Author
Li P
Kadirkamanathan V
Publication year
Publication venue
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)

External Links

Snippet

This paper presents the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. The FDI problem is formulated in the multiple model (MM) environment, then by combining the likelihood ratio …
Continue reading at www.fs.isy.liu.se (PDF) (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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Li et al. Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems
Sankararaman et al. Uncertainty in prognostics and systems health management
Youssef et al. An optimal fault detection threshold for early detection using Kullback–Leibler divergence for unknown distribution data
Mehranbod et al. Probabilistic model for sensor fault detection and identification
Sankararaman et al. Bayesian methodology for diagnosis uncertainty quantification and health monitoring
Fan et al. A sequential Bayesian approach for remaining useful life prediction of dependent competing failure processes
Sheppard et al. A Bayesian approach to diagnosis and prognosis using built-in test
Baghernezhad et al. Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots
Xu et al. Sensor fault detection and diagnosis in the presence of outliers
Sankararaman et al. Uncertainty quantification in structural damage diagnosis
Zhang et al. Fault detection and diagnosis based on particle filters combined with interactive multiple-model estimation in dynamic process systems
Dorj et al. A bayesian hidden markov model-based approach for anomaly detection in electronic systems
Kontar et al. Remaining useful life prediction based on the mixed effects model with mixture prior distribution
KR20170127430A (en) Method and system for detecting, classifying and / or mitigating sensor error
JP2019144779A (en) Causal estimation apparatus, causal estimation method, and program
Li et al. Prognostics of analog filters based on particle filters using frequency features
Jia et al. Active fault diagnosis for a class of closed-loop systems via parameter estimation
Honarmand-Shazilehei et al. Sensor fault detection in a class of nonlinear systems using modal Kalman filter
Prakash et al. Bayesian two-phase gamma process model for damage detection and prognosis
Aggoune et al. Change detection in a distillation column using non‐linear auto‐regressive moving average with exogenous input model and Hellinger distance
Abed et al. Data-driven power system operations
Roychoudhury Distributed diagnosis of continuous systems: Global diagnosis through local analysis
Li et al. Particle filtering based multiple-model approach to fault diagnosis in nonlinear stochastic systems
Shiri et al. Robust switching Kalman filter for diagnostics of long-term condition monitoring data in the presence of non-Gaussian noise
Yang et al. A Nonlinear Adaptive Observer‐Based Differential Evolution Algorithm to Multiparameter Fault Diagnosis