Open AccessDevelopment of Asynchronous Motor Bearing Fault Diagnosis Method using TDA and FFNN
Author(s) -
Amit Shrivastava
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9354.0881019
Subject(s) - asynchronous communication , artificial neural network , process (computing) , computer science , bearing (navigation) , fault (geology) , induction motor , reliability engineering , fault detection and isolation , vibration , control engineering , engineering , process engineering , actuator , artificial intelligence , voltage , electrical engineering , computer network , seismology , geology , operating system , physics , quantum mechanics
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).