Prof. Ananda Shankar Hati

Indian School of Mines, Indian Institute of Technology, Dhanbad 600036, India

Prof. Ananda Shankar Hati

Indian School of Mines, Indian Institute of Technology, Dhanbad 600036, India

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Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 June 2020 in Archives of Computational Methods in Engineering

Journal Article

Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

Prashant Kumar

Ananda Shankar Hati

1 May 2021 in ISA Transactions

Journal Article

Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM

Prashant Kumar

Ananda Shankar Hati

14 December 2020 in IET Electric Power Applications

Journal Article

Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor

Prashant Kumar

Ananda Shankar Hati

Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and selection. Considering these inherent traits of CNN, this study proposes a CNN in combination with batch normalisation (BN)‐based fault detection approach for simultaneous detection of bearing fault and broken rotor bars in squirrel cage induction motors (SCIMs). The SCIM vibration signals have different patterns for different defects, and the architecture of CNN is used in this study for fault diagnosis. For an efficient fault feature extraction, the proposed method uses CNN having multiple stacked layers with BN for faster training. In the proposed method, a CNN model with small kernel size is used along with adaptive gradient optimizer and BN to avoid performance degradation and optimum results. For the validation of the proposed technique, a test set‐up is used along with different fault conditions. The proposed method is also compared with the existing state‐of‐the‐art methods to illustrate its effectiveness.

1 April 2022 in Expert Systems with Applications

Journal Article

Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors

Prashant Kumar

Ananda Shankar Hati

1 August 2021 in Renewable and Sustainable Energy Reviews

Journal Article

A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence

Prince Kumar

Ananda Shankar Hati

9 December 2022 in Mathematics

Journal Article

Deep Transfer Learning Framework for Bearing Fault Detection in Motors

The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.

About Me

Dr Ananda Shankar Hati has been an Assistant Professor in the Department of Electrical Engineering of the IIT(ISM), Dhanbad, India, since 2013. He graduated with a B.Tech degree in Electrical Engineering in 2007 and an M.Tech degree in AEIE in 2009 from the WBUT. He received his PhD in Electrical Engineering from the IIT(ISM), Dhanbad, in 2016. Also, he completed his postdoctoral research at Deakin University in 2021. He is a Fellow of the Royal Society of Arts, UK, IETE, NBSP, a Chartered Engineer (India) in Electrical Engineering. He has authored or co-authored 28 research publications in technical journals, conference proceedings, and book chapters and published 35 Patents. He was a principal investigator on a research initiative funded by the TEQIP-II. Also, a Co-PI on a research initiative funded by the SERB, GoI. Furthermore, he was awarded an honorary D.Eng. and D.Sc. from the INSFC and DBHI, Iran. Also, he was appointed as an Honorary Adjunct Distinguished Scientist of DAAI of Thu Dau Mot University and Trisol, UK, Honorary Director, Center of Excellence in Robotics, Department of Mechatronics Engineering, School of Technology and Engineering of ITM(SLS) Baroda University and Honorary Adjunct Distinguished Professor, SIRG, Egypt. Current research in his laboratory emphasizes the multi-disciplinary environment focusing on machine intelligence, the Internet of Things, sensor networks applied to various real-world electrical engineering problems.

Prof. Ananda Shankar Hati

Prof. Ananda Shankar Hati

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About Me