Development of asynchronous motor bearing fault diagnosis method using TDA and FFNN

2Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

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).

Cite

CITATION STYLE

APA

Shrivastava, A. (2019). Development of asynchronous motor bearing fault diagnosis method using TDA and FFNN. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2622–2625. https://doi.org/10.35940/ijitee.J9354.0881019

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free