Multiple Faults Diagnosis of Induction Motor Using Artificial Neural Network

Jigyasu, Rajvardhan; Mathew, Lini

doi:10.1007/978-981-13-3140-4_63

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…Zhongming and Bin [12] suggested an ANN based approach for induction motor bearing fault diagnosis based on time, frequency and time-frequency domain analysis. Jigyasu et al, [13] presented a comparison of current and vibration based diagnosis using artificial neural networks for different types of motor failures such as bearings, stators, rotors and eccentricity. Nejjari and Benbouzid [14] proposed an IM fault diagnosis method based on the Park Vector Approach.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Chouhan¹,

Gangsar²,

Porwal³

et al. 2020

Vib. proced.

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This paper describes an Artificial Neural Network (ANN) based fault diagnosis methodology for Induction Motors (IM) operating under the same conditions for various speeds and loads. In this study, ten different IM fault conditions are considered. We considered five mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, bowed rotor, rotor with broken bar), four electrical faults (phase unbalance fault with two levels of severity, stator winding fault with two levels of severity), and one healthy motor condition. The current and vibration signals were considered in this work as these signals are generally considered to be the most efficient for the detection of mechanical and electrical faults in IM when used simultaneously. A machine fault simulator was used for the generation of vibration and current signals from different fault conditions. An ANN model was developed in which raw time domain vibration (in three directions) as well as current (in three phases) data are used simultaneously as input and then the fault diagnosis (training and testing) is performed. In this work, the fault diagnosis was attempted when testing was done for the same operating conditions as training. The developed fault diagnosis methods were found to be robust for various operating conditions (speeds and loads) of the IM.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Chouhan¹,

Gangsar²,

Porwal³

et al. 2020

Vib. proced.

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show abstract

“…Statistical values such as mean (

), variance (

), cluster shape (

), standard deviation (

), kurtosis (

), root mean square (

), and shape factor (

) are calculated from

. Equations ( 9 )–() show the statistical features, and they determine the evolution of signals in the time–space domain [ 2 , 8 ].

…”

Section: Methodsmentioning

confidence: 99%

“…Finally, an NN-based classifier is used to detect healthy induction motor and bearing faults from 1 mm to 5 mm with a class ratio greater than

. Similarly, in [ 8 ], eleven statistical features are extracted from electric current and vibration signals to diagnose six different faults with an ANN classifier. This method is developed in the time domain, asserting

.…”

Section: Introductionmentioning

confidence: 99%

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors

Contreras-Hernandez

Almanza-Ojeda

Ledesma

et al. 2022

Sensors

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Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.

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“…The general fault classification rate in AM was 97.45%. A multiple FD/D method using ANN was presented by Jigyasu, Mathew and Sharma (2018). The current and vibration responses of healthy motor, motor with bearing, rotor and stator defects were analyzed.…”

Section: Fd/d Based Onmentioning

confidence: 99%

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Drakaki

Karnavas

Tziafettas

et al. 2022

JIEM

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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 algorithmsFindings: 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 2015.

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Multiple Faults Diagnosis of Induction Motor Using Artificial Neural Network

Cited by 14 publications

References 17 publications

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

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