Performance Analysis of Motor Vibration Based Condition Monitoring Using R-curve
Traditional techniques of manually extracting characteristics from monitoring data need skill in signal processing and previous knowledge in failure detection, which is seldom possible on a machinery big data platform. As a result, a unique approach for automatically extracting adaptive fault characteristics from monitoring data and intelligently diagnosing fault patterns is projected to accomplish rotating equipment problem identification on a machinery big data platform. This study is focused on knowledge acquired from vibration analysis and applying towards condition monitoring techniques. Results showed 99.87% accuracy level of vibration that improves the performance of motor.
Authors
- Madeva Nagaral (ORCID: https://orcid.org/0000-0002-8248-7603)
- V. Auradi
- Santosh Janamatti (ORCID: https://orcid.org/0000-0003-2653-7239)
- Sameer Saadoon Algburi (ORCID: https://orcid.org/0000-0002-5483-6271)
- Yadavalli Basavaraj (ORCID: https://orcid.org/0000-0002-7595-2984)
- Nagaraj Namdev
- Hasan Sh. Majdi (ORCID: https://orcid.org/0000-0001-6752-4835)
- Fazil Nalband
- Salah J. Mohammed
- B. K. Pavan Kumar
Institutions
- University of Kirkuk (IQ)
- PES University (IN)
- Dijlah University College (IQ)
- Hindustan Aeronautics Limited (India) (IN)
- Siddaganga Institute of Technology (IN)
- Sri Siddhartha Medical College (IN)
Publication Details
- Journal
- Journal of Mines Metals and Fuels
- Published
- 2024-08-19
- DOI
- https://doi.org/10.18311/jmmf/2024/44562
- Primary Topic
- Infrastructure Maintenance and Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00