Multilevel failure detection system

Vaija, P.; Järveläinen, M.; Dohnal, Mirko

doi:10.1016/0166-3615(85)90047-8

Cited by 10 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amin et al applied fuzzy inference systems, knowledge fusion, and feature extraction to create a robust health monitoring system for the determination and classification of pump degradation [20]. For creating a failure detection system destined for complex processes such as a chemical plant, Vaija et al proposed the use of a multilevel fuzzy system [21]. Mechefske applied fuzzy logic techniques to classify frequency spectra that represent various rolling element bearing faults [22].…”

Section: Applications Of Fuzzy Logicmentioning

confidence: 99%

Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic

Holbert

Lin

2012

Science and Technology of Nuclear Installations

View full text Add to dashboard Cite

Economic constraints are driving the electric power industry to seek improved methods for monitoring, control, and diagnostics. To increase plant availability, various techniques have been implemented in industry to assess equipment condition to prevent system inoperability. The availability of a large number of measured signals and additional component information and the increasing number of signal processing options to analyze sampled data motivate the assimilation of such diverse information into a plantwide condition monitor. The use of fuzzy logic is described herein for the purpose of performing the decision making regarding the system status and the possible need for component maintenance. Fuzzy-logic-based diagnostic monitoring is applied to data acquired from instrumentation within operating facilities.

show abstract

Section: Applications Of Fuzzy Logicmentioning

confidence: 99%

Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic

Holbert

Lin

2012

Science and Technology of Nuclear Installations

View full text Add to dashboard Cite

show abstract

“…The qualitative approaches involve fault trees (Lees, 1983), signed directed graph (Kramer and Palowitch, 1987), functional decomposition (Finch and Kramer, 1988), fuzzy logic (Vaija, 1985), neural networks (Venkatasubramanian and Chan, 1989), and expert systems (Dhurjati et al, 1987). The quantitative approaches are basically modeling, filtering, and estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis in a closed-loop nonlinear distillation process: application of extended Kalman filters

Li¹,

Olson²

1991

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

A strategy for fault detection and diagnosis in a closed-loop nonlinear system is described. An extended Kalman filter (EKF) is applied inside the control loop. The EKF recovers information from noisy measurement signals, providing estimates of state variables and unknown parameters of the process. The state estimates produced by the EKF are the inputs to the controller. Since the noise in the measurements is reduced significantly, the control quality is much better than that achieved with application of exponential filters. The estimates of unknown process parameters, where the deviations from normal values are considered faults, are the basis to make alarm decisions. The EKF successfully tracks and distinguishes faults occurring simultaneously. The influences of correlated noises, a special issue of estimation in closed-loop systems, model errors, and number of measurements have been studied. The proposed strategy is applied to a 30-stage binary distillation column with a partial condenser and a reboiler. The simulation results are presented to show the effectiveness of the approach.

show abstract

Supervision, Fault-Detection and Fault-Diagnosis Methods

Isermann¹,

Füssel²

1999

Practical Applications of Fuzzy Technologies

View full text Add to dashboard Cite

Multilevel failure detection system

Cited by 10 publications

References 4 publications

Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic

Nuclear Power Plant Instrumentation Fault Detection Using Fuzzy Logic

Fault detection and diagnosis in a closed-loop nonlinear distillation process: application of extended Kalman filters

Supervision, Fault-Detection and Fault-Diagnosis Methods

Contact Info

Product

Resources

About