Accident identification in nuclear power plants using hidden Markov models

Kwon, Kee-Choon; Kim, Jinhyung

doi:10.1016/s0952-1976(99)00011-1

Cited by 51 publications

(16 citation statements)

References 9 publications

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“…HMMs were first used in prognostics by Bunks et al [72] and Baruah and Chinnam [73]. Since then both HMMs and HSMMs have been widely researched for both diagnostic and prognostic purposes, however to date most published results have been based on numerical simulations or artificially controlled experiments [58,70,72,[74][75][76][77][78][79][80]. Some practical examples using real-world data have been published however, such as [68,69], albeit in these cases, as part of an advanced air force prognostics programs.…”

Section: Hidden and Semi-hidden Markov Models Hidden Markov Models (mentioning

confidence: 99%

Prognostic modelling options for remaining useful life estimation by industry

Sikorska¹,

Hodkiewicz

2011

Mechanical Systems and Signal Processing

789

513

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Section: Hidden and Semi-hidden Markov Models Hidden Markov Models (mentioning

confidence: 99%

Prognostic modelling options for remaining useful life estimation by industry

Sikorska¹,

Hodkiewicz

2011

Mechanical Systems and Signal Processing

789

513

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“…Kwon and Kim (1999) used HMMs for accident identifi cation of a nuclear power plant by using a real-time test simulator including 22 variables. In another study (Ma and Shi, 2000), a stochastic approach based on HMM to distinguish a certain process behavior from a fault case is developed for transformer protection.…”

Section: Process Monitoring and Fault Diagnosis With Hmmsmentioning

confidence: 99%

Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

Tokatlı

Çınar

2004

Can J Chem Eng

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Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high‐temperature‐short‐time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.

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“…Since then, HMM has been widely used in several fields. Kwon et al [7] employed HMM to identify 9 types of accidents occurred in nuclear power plants using signals such as temperature and pressure. In this study, he extracted acceleration signal from a helicopter gear box to identify 8 kinds of defects.…”

Section: Introductionmentioning

confidence: 99%

Identification of location and size of a defect in a structural system employing active external excitation and hybrid feature vector components in HMM

2016

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For the fault diagnosis of a mechanical system, various kinds of methods have been developed so far. For a structural system having a defect, pattern recognition methods such as Hidden Markov model (HMM) and Artificial neural network (ANN) are widely used in engineering fields. A statistical model can be constructed with one of the methods using various signals that are extracted from the structural system of interest. In the present study, a HMM employing hybrid feature vector measures is proposed for the fault diagnosis of a structural system having a defect. To obtain the hybrid feature vector components, five frequency response peaks obtained with FFT and two additional components obtained with ANN are employed. For the proposed method, an active external excitation having some specific frequency components is also applied to the structure to overcome the noise effect. To verify the effectiveness of the proposed method, a numerical model of a rotating blade having a crack is employed. Acceleration signals extracted from the structural system are employed to develop the proposed model so that the location and size of the crack can be identified. Using the proposed method, the diagnostic accuracy of the identification is significantly improved even with high level of noise in the system.

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Accident identification in nuclear power plants using hidden Markov models

Cited by 51 publications

References 9 publications

Prognostic modelling options for remaining useful life estimation by industry

Prognostic modelling options for remaining useful life estimation by industry

Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

Identification of location and size of a defect in a structural system employing active external excitation and hybrid feature vector components in HMM

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