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

Chunzhen Yang, Jingquan Liu, Yuyun Zeng and Guangyao Xie

Renewable Energy, 2019, vol. 133, issue C, 433-441

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.

Keywords: Fault detection; Reconstruction model; Support vector regression; Sliding-time-window; Health index (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148118312461
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:133:y:2019:i:c:p:433-441

DOI: 10.1016/j.renene.2018.10.062

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().