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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Basavaraj, Yadavalli
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article
Performance Analysis of Motor Vibration Based Condition Monitoring Using R-curve
Abstract
<jats:p>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.</jats:p>