Development of Asynchronous Motor Bearing Fault Diagnosis Method using TDA and FFNN 
Amit Shrivastava
Amit Shrivastava, Department of Electrical Engineering, Poornima College of Engineering, Poornima University Jaipur, India.
Manuscript received on 15 July 2019 | Revised Manuscript received on 20 July 2019 | Manuscript published on 30 August 2019 | PP: 2622-2625 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93540881019/19©BEIESP | DOI: 10.35940/ijitee.J9354.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Asynchronous motors (AM) are life line of any process industry. Malfunctioning of AM at any stage of process leads the cost of finish product and decrease the efficiency of plant. Hence detection and diagnosis of AM failure at early stage is essential for timely maintenance and enhance the overall efficiency of the plant. The work present in this paper focuses on the bearing faults of AM. For this purpose experimental setup is developed in laboratory and results are based on experimental study carried out in laboratory by analysing AM generated vibration signals using time domain analysis (TDA) and feed forward neural network (FFNN).
Index Terms: Bearing Failure, Induction Motor, Neural Network, Time Domain Analysis, Vibration Signals.
Scope of the Article: Recent Trends & Developments in Computer Networks