An approach for bearing fault detection in electrical motors

Gülmezoğlu, M. Bilginer; Ergin, Semih

doi:10.1002/etep.161

Cited by 16 publications

(15 citation statements)

References 22 publications

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“…CVA is a subspace-based pattern recognition method that has been preferred for use in speech recognition (Gülmezoglu, Dzhafarov, & Barkana, 2001;Gülmezoglu, Dzhafarov, Keskin, & Barkana, 1999), speaker recognition (Gülmezoglu & Barkana, 1998), image recognition (Çevikalp, Neamtu, Wilkes, & Barkana, 2005), and motor fault diagnosis (Gülmezoglu & Ergin, 2007) problems. In the implementation of CVA, a common vector for each class is calculated using feature vectors in the training set of that class.…”

Section: Common Matrix Approach Using Gram-schmidt Orthogonalization mentioning

confidence: 98%

A new implementation of common matrix approach using third-order tensors for face recognition

Ergin

Cakir

Gerek

et al. 2011

Expert Systems with Applications

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Section: Common Matrix Approach Using Gram-schmidt Orthogonalization mentioning

confidence: 98%

A new implementation of common matrix approach using third-order tensors for face recognition

Ergin

Cakir

Gerek

et al. 2011

Expert Systems with Applications

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“…The SVM and ANN methods are most widely used in the studies of classification of the short circuits occurring in transmission lines. The CVA classifier was used in the diagnosis of the induction motor-bearing faults [29], phaseground short circuit in low-voltage systems, and classification of four different faults exposed to two different loads [30]. In this paper, short circuits that occurred in high-voltage transmission line are classified.…”

Section: Classifiers and Graphical User Interface Designmentioning

confidence: 99%

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Yumurtacı¹,

Gökmen²,

Kocaman³

et al. 2016

Turk J Elec Eng & Comp Sci

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The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components.Constructed feature vectors are treated with a classifier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CVA). This is the first implementation of CVA in the classification of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and artificial neural network for a comparison with the CVA method regarding classification performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classification of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. It is concluded that a combination of the proposed feature extraction scheme with the CVA classifier gives substantially high performance for the classification of short circuit faults in transmission line.

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“…The burgeoning use of IMs in the industries is standing upon the pillars of efficient, continual, and cost‐effective operation of bearings. So, to have the incessant production, flawless operation of bearings is imperative . Here, condition monitoring, commonly known as predictive maintenance, steps in.…”

Section: Introductionmentioning

confidence: 99%

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

Saini

Aggarwal

2018

Int Trans Electr Energ Syst

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Summary A novel framework is proposed for the classification of motor bearing faults by using multiwavelet transform and naive Bayes classifier. This work has explored the application of multiwavelet transform to the vibration signatures for extracting the effective fault features. Geronimo‐Hardin‐Massopust multiwavelet filter bank has been employed in this work for multiresolution analysis up to fourth decomposition level. This work has relied upon multiwavelets as the multiwavelets are enriched with the properties which wavelets do not offer simultaneously like compact support and orthogonality without compromising with symmetricity and higher order of vanishing moments. After that, fault features have been extracted from the decomposed vibration signals to utilize the fault information present at different resolution levels. The fault classification has been accomplished with high success rate by using the extracted fault features with the naive Bayes classifier due to its inherent features like robustness to noise, simplicity, and quick convergence even with less training data. The efficacy of the proposed classifier has been verified with both the offline signals and the real‐time signals having different fault severity levels. The outperformance of the proposed classifier has been shown by comparing the classification results with other established supervised classification techniques like probabilistic neural network, Gaussian‐support vector machine, polynomial‐support vector machine, and linear‐support vector machine.

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An approach for bearing fault detection in electrical motors

Cited by 16 publications

References 22 publications

A new implementation of common matrix approach using third-order tensors for face recognition

A new implementation of common matrix approach using third-order tensors for face recognition

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

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