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This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.

First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs.

The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach.

The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

The purpose of this paper is to examine the long-run association and short-run causality among oil price, exchange rate and stock market in Norwegian context.

This work uses auto regressive distributed lag (ARDL) bound co-integration test to examine the long-run association among international crude oil, exchange rate and Norwegian stock market. Further to test the causality, Toda–Yamamoto Granger causality test is used. Daily data ranging from 1 January, 2011 to 31 December, 2018 is used in this study.

Findings of this study suggest the existence of long-run equilibrium relationship among oil price, exchange rate and Norwegian stock market when oil price is taken as dependent variable. Further, this study observes the bi-directional causality between Norwegian stock market and exchange rate and unidirectional causality between oil and Norwegian stock market (from oil to stock market).

To the best of the authors’ knowledge, this the first study in context of Norway to explore the long-run association and causal relationships among international crude oil price, exchange rate and stock market index. Particularly, association of exchange rate and stock market largely remains unexplored for Norwegian economy. Further, majority of studies conducted in Norwegian setup have considered the period up to year 2010 and association of these variables is found to be time varying. Finally, this study uses ARDL bound co-integration test and Toda–Yamamoto Granger causality test. These methodologies have been used in literature in context of other countries like India and Mexico but not yet applied to study the Norwegian case.

This paper aims to analyze the impact of the global financial crisis on the conditional beta in the region of North America and Western Europe and the effect on the behavior and decisions of the investor.

The authors model the variations of volatility in financial markets during crisis using the bivariate GARCH model of Engle and Kroner (1995).

The empirical investigation identifies an additional effect of the crisis over the period of the test. Results indicate a rise in the beta in some cases and a fall in others. This rise had a direct impact on the systematic beta risk, which increased for the majority of the companies during the crisis period. The increase in beta during the crisis period has an effect on the behavior of the investor and his decisions.

The increase in the beta during the period of crisis due to a high volatility returns has an effect on the behavior and decisions of the investor.

This paper examines the effects of the “subprime crisis” on the risk premium of companies in several sectors of activity.