Optimization of sensor location for improving wind power prediction accuracy

Jiang, Zhaoyu; Jia, Qing‐Shan; Guan, Xiaohong

doi:10.1109/coase.2017.8256267

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the possibility of misclassification, more vibration signal fusion needs to be considered. In addition, the sensor installation location to reduce the redundancy of fusion data was studied [23,24].…”

Section: Introductionmentioning

confidence: 99%

Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

Cheng

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

show abstract

Section: Introductionmentioning

confidence: 99%