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- W2954554785 abstract "As the industrial system evolved increasingly sophisticated in recent years, fault detection and diagnose has attracted extensive attentions either in academic or industrial field. This paper proposes a novel method of fault detection and classification that applied to an industrial system such as DC motor. The key point to this methodology is choosing the reciprocals of H ∞ -norm of the system and its corresponding subsystems as the feature quantities of the classifier. In the actual production process, system parameters will inevitably change. Hence, a data-driven solution is indispensable to estimate the real H ∞ -norm of the system and corresponding subsystems. Furthermore the reciprocals of those real-time H ∞ -norm of system are fed into a SVM classifier to identify the type of fault. The simulation studies based on a double loop DC motor system indicate that the scheme proposed in this paper has a relatively good accuracy and convergence." @default.
- W2954554785 created "2019-07-12" @default.
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- W2954554785 date "2019-02-01" @default.
- W2954554785 modified "2023-09-26" @default.
- W2954554785 title "A Novel Data-Driven Fault Classification Method and its Application to DC Motor" @default.
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- W2954554785 doi "https://doi.org/10.1109/icit.2019.8754993" @default.
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