Kernel latent features adaptive extraction and selection method for multi-component non-stationary signal of industrial mechanical device

摘要

Heavy key mechanical devices relate to production quality and quantity of complex industrial process directly. It is necessary to estimate some difficulty-to-measure process parameters inside these devices. Multi-component and non-stationary mechanical signals, such as vibration and acoustic ones, are always employed to model these process parameters indirectly. How to effective extract and select interesting information from these signals is the key step to build effective soft sensor model. In this paper, a new kernel latent features adaptive extraction and selection method is proposed. Ensemble empirical mode decomposition (EEMD) is used to decompose these mechanical signals into multiple time scales sub signals with different physical interpretations. These sub-signals are transformed to frequency spectra, and then kernel partial least squares (KPLS) algorithm is used to extract their kernel features. Integrated with mutual information (MI)-based feature selection method, a new define index is exploited to select the important sub-signals and their latent features adaptively. The shell vibration and acoustic signals of an experimental laboratory-scale ball mill in the mineral grinding process are used to validate the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.