Detrending and Denoising of Industrial Oscillation Data

Industrial oscillation recordings are often corrupted by underlying nonstationary trend and noisy artifacts, which can occlude features of interest and complicate subsequent oscillation detection and diagnosis. However, there are considerably fewer techniques available in the literature for systematically removing both trend and noise terms from the oscillation measurements. To cater for the general detrending and denoising needs of oscillatory signals, this article proposes an integrated framework featuring the following steps: 1) The ensemble empirical mode decomposition is first adopted to decompose the industrial single-loop data into several intrinsic mode functions (IMFs). 2) To eliminate the trend term, a surrogate-based nonstationarity testing algorithm is implemented to automatically identify and remove the requisite IMFs. 3) By applying canonical correlation analysis on the remained IMFs, the noise-dependent components can be further isolated, which finally yields the detrended and denoised oscillation data. We conducted performance comparison study through extensive simulations and industrial examples. The results demonstrate that the proposed work is a promising tool for industrial oscillation data preprocessing.