Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

Füssel, Dominik; Isermann, Rolf

doi:10.1109/iecon.1998.723027

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…On the other hand, fuzzy systems provide clear advantages in knowledge representation and acquisition. For example, knowledge is easily introduced in parallel to an adaptive fuzzy neural network by constructing a hierarchical diagnosis structure and modifying rules by available structured knowledge [96], or modifying and adjusting fuzzy inference for pattern recognition with lack of input data by some complementary knowledge [7]. Fuzzy systems, however, have been missing adaptation capabilities for a long time [1].…”

Section: Industrial Innovation Using Soft Computingmentioning

confidence: 99%

“…Power electronics and motion control have been the basic implementation technologies for robotics and automation. Human-friendly diagnosis technology in which intelligent information processing is essential is increasingly being developed [93], [95], [96]. Table 7 presents a summary of the power electronics and motion control, and the system characteristics that have been made possible mainly by soft computing, most of which are implemented using commercially available digital signal processors.…”

Section: B Application Fieldsmentioning

confidence: 99%

“…Structure knowledge and experimental data were used for a decision tree. As a conclusion, soft computing methods will be increasingly applied to challenging diagnosis problems, leading to human friendly motor drive systems [96].…”

Section: F Diagnosismentioning

confidence: 99%

See 2 more Smart Citations

Industrial applications of soft computing: a review

Dote

Ovaska²

2001

Proc. IEEE

136

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Section: Industrial Innovation Using Soft Computingmentioning

confidence: 99%

Section: B Application Fieldsmentioning

confidence: 99%

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Industrial applications of soft computing: a review

Dote

Ovaska²

2001

Proc. IEEE

136

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“…Such electromechanical systems are used in many high-risk industrial applications, due to their low price and to their low power consumption. Several different techniques have been developed to analyze the faults of DC motors [1,13,21,4]. Fault detection methods are generally divided into two classes: methods based on signal analysis and methods based on motor dynamical models.…”

Section: Introductionmentioning

confidence: 99%

Application of algebraic geometry techniques in permanent-magnet DC motor fault detection and identification

Menini

Possieri

Tornambè

2015

European Journal of Control

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“…Thus, the main problem of the fuzzy-logic-based schemes that use an explicit knowledge base (fuzzy rule bases) is, in many cases, the lack of sufficient information about the fault symptom relationship. Moreover, only parts of a knowledge base exist, or they are tedious to derive [11].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based Fault Classification Scheme for a Transmission Line Using a Support Vector Machine

Bhalja

Maheshwari

2008

Electric Power Components and Systems

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This article presents a new approach for fault classification in a twoterminal overhead transmission line using a support vector machine classifier. Wavelet transform is used for the decomposition of measured signals and for extraction of the most significant features (feature extraction), which facilitates training of the SVM, particularly in terms of getting better classification performance (high accuracy). After extracting useful features from the measured signals, a decision of fault or nofault on any phase or multiple phases of a transmission line is carried out using three SVM classifiers. The ground detection task is carried out by a proposed ground index. Two kernel functions-polynomial and Gaussian radial basis function (RBF)-have been used, and performances of classifiers have been evaluated based on fault classification accuracy. In order to determine the optimal parametric settings of an SVM classifier (such as the type of kernel function, its associated parameter, and the regularization parameter C ), five-fold cross-validation has been applied to the training set. It is observed that an SVM with an RBF kernel provides better fault classification accuracy than that of an SVM with polynomial kernel. One of the key points of this article is the development of an automatic fault data generation model using PSCAD and its application for training and testing of SVMs. To illustrate the effectiveness of the proposed scheme, extensive simulations have been carried out for different fault conditions with wide variations in the operating conditions and source impedances. It has been found that the proposed scheme is very fast and accurate, and it proved to be a robust classifier for digital distance protection.

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Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

Cited by 16 publications

References 19 publications

Industrial applications of soft computing: a review

Industrial applications of soft computing: a review

Application of algebraic geometry techniques in permanent-magnet DC motor fault detection and identification

Wavelet-based Fault Classification Scheme for a Transmission Line Using a Support Vector Machine

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