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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.
Kumar et al. (Mon,) studied this question.