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Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model

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Publication date: April 2019

Source: Renewable Energy, Volume 133

Author(s): Chunzhen Yang, Jingquan Liu, Yuyun Zeng, Guangyao Xie

Abstract

Reconstruction model is a powerful method for component condition monitoring and fault detection by considering the model prediction residuals. In this article, a new signal reconstruction modeling technique is proposed using support vector regression. Multiple indicators are calculated to recognize slight shift from normal condition, and detect the fault at an early stage. Input variables are selected based on correlation analysis and failure mode analysis. A sliding-time-window technique is employed to incorporate temporal information inherent in time-series data. Residuals between the observed signal and the reconstruction signal are utilized to indicate whether the desired quantity is different from its normal operation condition or not. Three statistical indicators (Deviation Index, Volatility Index and Significance Index) are defined to quantify the deviation level from normal condition to abnormal condition. Health index (HI) of a specific fault is derived from responsive statistical indicators, and the integral health index (integral-HI) of an entire component is composed of all individual health index. An experiment of real-life wind turbine high temperature fault detection scheme is studied. Results show that the proposed approach demonstrates improved performance in detecting wind turbine faults, and controlling false and missed alarms.

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