Vibration signature analysis by hybrid multi-layer neuro-fuzzy system

Sumit Kumar Sar, Ramesh Kumar


A proficient fault detection model has to be sketched for detecting slight variations of the vibrating signal of rotating machine whereas the diagnosis process prominently stuck with the inefficient extraction of effectual features of a signal in reduced time. Existence of above stated issue results in the confinement of inventive Module 1 of the Vibration Signature Analysis by Hybrid Multi-Layer Neuro – Fuzzy System (V-HMNFS), which could collect the RKC (RMS, Kurtosis, Crest factor) signal features for every instantaneous signal unit while eliminates noise thereby reducing pre-processing task. This in turn lessens time consumption and at the end yields learnt extracted faulty features. Accurate classification of faulty features can be accomplished by casting inimitable Module 2 classifier which provokes a good path to provide accurate classification based on learnt features. This responsible classifier collectively organises the RKC features of respective signal units and does accurate classification of faulty occurrences based on the features in less time.

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Published: 2020-12-29

How to Cite this Article:

Sumit Kumar Sar, Ramesh Kumar, Vibration signature analysis by hybrid multi-layer neuro-fuzzy system, J. Math. Comput. Sci., 11 (2021), 635-660

Copyright © 2021 Sumit Kumar Sar, Ramesh Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

J. Math. Comput. Sci.

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