Abstract
As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.
Highlights
At present, the environmental pollution and the energy shortage become increasingly prominent, while wind power as a representative of the new clean energy has made a leap forward in recent years
With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective
Multivariate time series processing method based on Riemannian manifold is based on sliding window and uses the covariance matrix as a descriptor of the time sequence, in which Riemannian distance is used as the similarity measure and the statistical process control diagram as evaluation to detect the abnormity of multivariate time series
Summary
The environmental pollution and the energy shortage become increasingly prominent, while wind power as a representative of the new clean energy has made a leap forward in recent years. Finland, and Germany’s wind power failure statistics data show that failures will occur in the wind turbine blade, hydraulic, electrical, and machine driven systems, in which the most common one is the electrical system fault and the major downtime is caused by gear faults. Researching the more accurate wind turbine fault diagnosis technology, improving the operation’s reliability of wind power turbines, and realizing the conversion from. Failure is made to be found in time and downtime accidents are prevented by researching the more accurate wind turbine fault diagnosis technology, improving the operation’s reliability of wind power turbines, and realizing the conversion from the preventive maintenance to the state detection. Based on the wind turbine operation and the analysis of the structural characteristics, the goal of the researchers becomes how to improve the efficiency of fault diagnosis and visualization. The method realized wind turbine gearbox fault diagnosis correctly
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