Support Vector Based Novelty Detection for Fault Tolerant Control

Saludes, Sergio; Fuente, José Luis de la

doi:10.1109/cdc.2005.1583091

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…In The FDD operation of the method can be explained with an example. A leakage of r 1 = 4 mm in tank 1 within the period of time t = [5000-10,000], an actuator fault of K f = 0.3 within the period of time t = [15,000], and a leakage of r 2 = 5 mm in tank 2 within the period of time t = [32,000-40,000] are assumed to occur. The input signal, estimated boundaries, and nonfaulty case response are given in Figure 11.…”

Section: The Results Of the Methodsmentioning

confidence: 99%

“…In active FTC systems, the reconfiguration mechanism can be classified as on-line controller selection and on-line controller calculation techniques [14]. In the on-line controller selection approach, the controllers associated with cer-tain/predetermined faulty conditions are computed in an off-line manner in the design stage and they are selected in an on-line manner based on real-time data from the FDD algorithm [15]. In the on-line controller calculation approach, the controller parameters are calculated in an on-line manner right after the occurrence of the fault [14,16].…”

Section: Introductionmentioning

confidence: 99%

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A fault detection, diagnosis, and reconfiguration method via support vector machines

Kabaoğlu¹

2015

Turk J Elec Eng & Comp Sci

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This paper presents a fault detection, diagnosis, and reconfiguration method based on support vector machines. This method is appropriate for certain or predetermined faults and involves a fault detection and diagnosis unit and an online controller selection type reconfiguration mechanism. In this method, when a fault is detected and diagnosed by the fault detection and diagnosis unit, a suitable controller, which has been determined via an optimization algorithm in an off-line fashion, is activated to maintain proper closed-loop performance of the system in an on-line manner. In the detection, diagnosis, and reconfiguration stages of the method, support vector classification and regression machines are used and the performance is tested on a simulation model of a two-tank level control system for various fault scenarios.

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Section: The Results Of the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fault detection, diagnosis, and reconfiguration method via support vector machines

Kabaoğlu¹

2015

Turk J Elec Eng & Comp Sci

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“…The application of neural networks in fault diagnosis of chemical process focuses on the following aspects such as using as a classifier, using as a dynamic forecast model, and combining with other diagnostic methods. Later, pattern classification and model identification [4], EKF based fault detection [5], and fault diagnosis of ball bearing using machine learning method [6] were used.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Batch Reactor Using Machine Learning Methods

Subramanian¹,

Ghouse²,

Pappa

2014

Modelling and Simulation in Engineering

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Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM) is used to estimate the heat release (Qr) of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faultyQrvalues, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter (Qr) shows that it is more efficient and fast for diagnosing the typical faults.

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“…En (Saludes and Fuente, 2005) utilizan el SVM para detección de novedades en un esquema de control tolerante a fallos. En (Qiu et al, 2003), (Hu et al, 2007), (Pan et al, 2009) se utiliza la transformada Wavelet para mejorar el diagnóstico de los fallos utilizado SVM, obteniéndose resultados que permiten separar las diferentes condiciones de fallos y la gravedad de los fallos incipientes.…”

Section: Métodos Basados En Conocimientounclassified

Aplicación de técnicas robustas para detección y diagnóstico de fallos

Berbesi¹

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Support Vector Based Novelty Detection for Fault Tolerant Control

Cited by 3 publications

References 16 publications

A fault detection, diagnosis, and reconfiguration method via support vector machines

A fault detection, diagnosis, and reconfiguration method via support vector machines

Fault Diagnosis of Batch Reactor Using Machine Learning Methods

Aplicación de técnicas robustas para detección y diagnóstico de fallos

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