Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the h-th harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5th harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5th harmonic. The results demonstrated that the positive-sequence magnitude for the 5th harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Detecção de falha do estator de um motor de indução trifásico utilizando uma bobina exploratória externa

Este trabalho apresenta uma metodologia para a detecção de falhas de curtos-circuitos entre as espiras de motores de indução trifásicos, utilizando uma bobina exploratória externa, que é uma técnica não invasiva e pode ser utilizada durante a operação do motor. O fluxo magnético de dispersão do motor, operando em condições de curto-circuito, induz uma tensão na bobina exploratória que difere de um padrão de referência, que é correspondente ao motor operando de forma saudável. Os dados experimentais foram obtidos numa bancada de teste, composta por um motor de indução trifásico com rotor gaiola de esquilo de 0,75kW. O enrolamento do estator desse motor é modificado para permitir a introdução de curto-circuitos. Este trabalho considerou os curto-circuitos em uma fase, com 1%, 3%, 5% e 10% das espiras, com o motor operando com carga variável. O diagnóstico de curto-circuitos são obtidos através da análise de diferentes topologias de redes neuronais artificiais perceptron multicamadas, com os dados experimentais utilizados em três abordagens diferentes. A primeira abordagem consiste em utilizar os dados no domínio do tempo, na segunda abordagem é realizado a transformada de Fourier dos dados e é coletado a energia numa banda de frequências e na terceira abordagem, também é realizado a transforma de Fourier dos dados, sendo utilizadas as amplitudes dos harmónicos. Os resultados obtidos demonstram que a metodologia proposta apresenta dificuldades em identificar falhas em estágios incipientes, mais precisamente os curto-circuitos de 1%, entretanto para os de curto-circuito de 10%, a taxa de exatidão das redes neuronais foi de 100%. Dentre as 3 abordagens testadas na utilização dos dados, a abordagem que utiliza as amplitudes dos harmónicas foi a que apresentou a melhor eficácia no diagnóstico de curto-circuito no enrolamento do estator. Concretamente, com a melhor topologia obtiveram-se exatidões de 87% e 95% para os casos da utilização e sem utilização, respetivamente, das amostras com curto-circuito de 1%.This work presents a methodology for detecting inter-turns short-circuit fault of three-phase induction motors, using an external search coil, which is a non-invasive technique and can be used during the operation of the motor. The dispersion magnetic flux of the motor operating in short-circuit conditions induces a voltage in the search coil that differs from a reference pattern, which corresponds to the motor operating healthily. The experimental data were obtained in a test bench, using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered the short-circuits in one phase, with 1%, 3%, 5% and 10% of the turns, with the motor operating with variable load The diagnosis of short circuits is obtained through the analysis of different topologies of multilayer perceptron artificial neuronal networks, with the experimental data used in three different approaches. The first approach consists of using the data in the time domain, in the second approach the Fourier transform of the data is performed and energy is collected in a frequency band and in the third approach, the Fourier transform of the data is also performed, being used the amplitudes of the harmonics. The results obtained demonstrate that the proposed methodology presents difficulties in identifying flaws in incipient stages, more precisely short circuits of 1%, however for short circuits of 10%, the accuracy rate of neural networks was 100%. Among the three approaches tested in the use of data, the approach that uses the amplitudes of the harmonics was the one that showed the best efficiency in the diagnosis of a short circuit in the stator winding. Specifically, with the best topology, accuracy of 87% and 95% was obtained for the cases of use and without use, respectively, of the samples with a short circuit of 1%