Pre-processing approach for de-noising on-line oil chromatography data based on self-adapting wavelet analysis

Abstract

Due to the influence of the external environment and the performance of measuring equipment, the on-line oil chromatography data contains obvious noise which makes the signal to oscillate. The monitoring data is difficult to be directly applied to the equipment state analysis. A novel wavelet-based de-noising method is proposed for pre-processing the on-line oil chromatography data. By analysing the characteristics of on-line oil chromatography data, the method of determining the decomposition layer number based on the probability distribution of wavelet coefficients and the method of determining the threshold value based on outliers conservation are proposed. The improved wavelet de-noising method is applied to analysing the on-line oil chromatography data of a defective ultra-high voltage (UHV) reactor. The results show that the proposed method is feasible and effective.

Keywords

• wavelet transforms
• chromatography
• signal denoising
• reactors (electric)
• insulating oils
• power engineering computing
• novel wavelet-based denoising method
• online oil chromatography data pre-processing approach
• self-adapting wavelet analysis
• monitoring data
• equipment state analysis
• decomposition layer number
• probability distribution
• threshold value
• outlier conservation
• defective UHV reactor