During the daily work of thermal power plants

DCS monitoring signal is usually mixed up with massive varieties of noise interference inevitably. Due to the complex working condition

time series signal has a feature of chaotic. In order to meet the requirements of accuracy and validity for data application

it is necessary to denoise the original collected data. Chaotic time series are generally processed in phase space

manifold learning is applied to carry out dimension reduction

thus to eliminate the noise in high dimension and preserves the desired signal in low dimension for denoising. Based on the research of LLP

the authors define cosine distance in Euler representation instead of Euclidean distance along with orthogonal constraint in process of projection to preserve the nonlinear characteristic of the original manifold and deal with concentrated projection of adjacent vector. The algorithm is simulated and applied in monitoring data cleaning of coal mill

and the results show that

compared with wavelet noise reduction and LLP

this noise reduction method can better repair the overall manifold structure of the phase space

make it clearer

more smooth

and retain more useful signals and their nonlinear characteristics while filtering out high-frequency noise. This algorithm makes the prediction model more accurate and faster in computation.