Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in SCIM

Kumar, Prashant; Hati, Ananda Shankar; Padmanaban, Sanjeevikumar; Leonowicz, Zbigniew; Chakrabarti, Prąsun

doi:10.1109/eeeic/icpseurope49358.2020.9160712

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Kumar et al 2020 61 introduced a novel transfer learning (TL) and a DCNN to detect bearing fault and BRB faults (individually and jointly) in squirrel cage induction motors. TL extracts critical information from the already trained model to improve the learning of a new model therefore it is useful for less labelled data cases.…”

Section: Based Motor Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

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Section: Based Motor Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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“…The power consumption by SCIM accounts for almost 70% of total power consumption in the industries. [1][2][3] The condition monitoring of SCIM in the industries is indispensable for the tiniest interruption and supreme production. It ensures the unwavering quality and well-being of the industrial environment.…”

Section: Introductionmentioning

confidence: 99%

“…Many industries rely on it, including mining, oil, cement, textiles, and many more. The power consumption by SCIM accounts for almost

70\%

of total power consumption in the industries 1‐3 . The condition monitoring of SCIM in the industries is indispensable for the tiniest interruption and supreme production.…”

Section: Introductionmentioning

confidence: 99%

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A transfer learning‐based deep convolutional neural network approach for induction machine multiple faults detection

Kumar

Hati

Kumar

2023

Adaptive Control & Signal

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SummaryThe condition monitoring of squirrel cage induction motors (SCIMs) is vital for uninterrupted production and minimum downtime. Early fault detection can boost output with minimum effort. This article combines the application of transfer learning and convolution neural network (TL‐CNN) for developing an efficient model for bearing and rotor broken bars damage identification in SCIMs. A simple technique for the 1‐D current signal‐to‐image conversion is also proposed to provide input to the proposed deep learning‐based TL‐CNN technique. The proposed approach embodies the advantages of TL and CNN for effective fault identification in SCIMs. The developed technique has classified faults efficiently with an average accuracy of 99.40%. The complete analysis and data collection have been done on the experimental set‐up with a 5 kW SCIM and LabVIEW‐based data acquisition system. The propounded fault detection model has been created in python with the help of packages like Keras and TensorFlow.

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An Intelligent System for Bearing Fault Diagnosis of Induction Motor using Wavelet Transform Based Deep Learning Framework

Ray¹,

Ganguly²

2022

2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)

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Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in SCIM

Cited by 9 publications

References 17 publications

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A transfer learning‐based deep convolutional neural network approach for induction machine multiple faults detection

An Intelligent System for Bearing Fault Diagnosis of Induction Motor using Wavelet Transform Based Deep Learning Framework

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