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• W2896214849 abstract "In the era of big data, a huge amount of monitoring and manufacturing data is generated every hour. As these data are typically measured from different machines and under different working regimes, prior information and domain knowledge are highly required in order to properly analyze and utilize these data. In view of this limitation, a data-driven self-comparison approach is proposed for the monitoring of rotating machinery. In this approach, comb filtering is introduced to extract the concerned signals from multisource background noise. A Gini-guided residual singular value decomposition is then proposed to enhance local anomalies induced by early defects. Finally, an iterative Mahalanobis distance is constructed to measure the statistical deviation of monitored component from a normal state. With the proposed method, health monitoring of rotating machinery could be achieved without prior information and domain knowledge, thereby providing an automatic data processing and condition monitoring tool in big data context." @default.
• W2896214849 created "2018-10-26" @default.
• W2896214849 creator A5056499189 @default.
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• W2896214849 date "2019-04-01" @default.
• W2896214849 modified "2023-10-17" @default.
• W2896214849 title "A Data-Driven Monitoring Scheme for Rotating Machinery Via Self-Comparison Approach" @default.
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• W2896214849 doi "https://doi.org/10.1109/tii.2018.2875956" @default.
• W2896214849 hasPublicationYear "2019" @default.
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