Informatics Publishing Limited

V. N. Ghate ¹, S. V. Dudul ²

Affiliations
1 Electrical Engineering Department, Government College of Engineering, Amravati (MS), IN
2 Applied Electronics Department, Sant Gadge Baba Amravati University, Amravati

Abstract

Early detection of abnormal conditions during induction motor's operation would eliminate consequential damages on motor, so that outage time and costs of repairing can be reduced. Due to unique fingerprints from faults in line currents, it is possible to detect faults by extracting fault information from line currents. From the literature review it is observed that in many model based on ANN based techniques, the stator current spectra is used as input feature space. In this paper, simple thirteen statistical parameters are used as an input feature space. Support Vector Machine (SVM) is proposed as a fault classifier. Since the basic SVM is essentially a 2-class classifier, the synergism of three classifiers is proposed to overcome the limitation. Principal Component Analysis (PCA) is used as data fusion method to reduce the dimension of classifier. To verify the performance various kernel function as (Radial Basis Function (RBF), Quadratic, Linear, Polynomial, Multilayer Perceptron (MLP)) are applied and tested with real experimental datasets. In order to generate the experimental data, specially designed 2 HP, three phase, 4 pole, 415V, 50 Hz induction motor is used.

Keywords

Induction Motor, Fault Classification, SVM, PCA

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Ranjana Raut ¹, S. V. Dudul ¹

Affiliations
1 P. G. Department of Applied Electronics, SGB Amravati University, Amravati- 444602 (M.S.), IN

Abstract

Experiments with the Switzerland heart disease database have concentrated on attempting to distinguish presence and absence. The classifiers based on various neural networks, namely, MLP, PCA, Jordan, GFF, Modular, RBF, SOFM, SVM NNs and conventional statistical techniques such as DA and CART are optimally designed, thoroughly examined and performance measures are compared in this study. With chosen optimal parameters of MLP NN, when it is trained and tested over cross validation (unseen data sets), the average (and best respectively) classification of 98±2.83 % (and 100%), 96.67±4.56% overall accuracy, sensitivity 96±5.48, specificity 100% are achieved which shows consistent performance than other NN and statistical models. The results obtained in this work show the potentiality of the MLP NN approach for heart diseases classification.

Keywords

Heart Disease, MLP Neural Network, Error Back Propagation Algorithm, Performance

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S. N. Naikwad ¹, S. V. Dudul ¹

Affiliations
1 Dept. of Electrical Engineering, College of Engineering & Technology, Babhulgaon, Akola-444 104, IN

Abstract

In this paper, a novel focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical magnetic stirrer process. Magnetic stirrer exhibits complex nonlinear operations where reaction is exothermic. It appears to us that identification of such a highly nonlinear system is not yet reported by other researchers using neural networks. As magnetic stirrer process includes time relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE and correlation coefficient on testing data set. Finally, effect of different norms are tested along with variation in gamma memory filter. It is shown that dynamic NN model has a remarkable system identification capability for the problem considered in this paper. Thus, FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is major contribution of this paper.

Keywords

Magnetic Stirrer, Focused Time Lag Recurrent Neural Network, Gamma Memory Filter

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