Performance Analysis of Motor Vibration Based Condition Monitoring Using R-curve
DOI:
https://doi.org/10.18311/jmmf/2024/44562Keywords:
Condition monitoring, FFT Analyzer, Maintenance, Neural Network, VibrationAbstract
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.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2024-07-19
Published 2024-08-19
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