Mechanical　vibration　&　acoustic　signals　with　characteristics　of　multiple　components,　nonstationarity　and　nonlinearity　are　always　used　to　construct　the　data-driven　soft　sensor　model　of　industrial　processes.　It　is　one　of　the　main　approaches　to　measure　the　difficulty-to-measure　process　parameters　inside　those　high　energy　consumption　mechanical　devices.　Duo　to　the　complexity　of　the　production　mechanism　of　these　mechanical　signals,　most　of　these　soft　sensor　models　are　difficult　to　be　explained.　Moreover,　the　characteristics　of　the　industrial　process＇　continuous　running　and　the　mechanical　equipment＇　operation　modes　lead　to　the　difficulty　of　high　economic　cost　and　long　period　waiting　to　obtain　sufficient　training　samples.　To　solve　these　problems,　a　new　multi-component　mechanical　signal　modeling　method　based　on　virtual　sample　generation　(VSG)　technology　is　proposed.　Firstly,　the　mechanical　signals　are　processed　into　a　set　of　sub-signals　with　different　time　scales　by　using　adaptive　multi-component　signal　decomposition　technique;　then　these　sub-signals　are　transferred　to　high　dimensional　multi-scale　spectral　data.　Secondly,　an　improved　selective　ensemble　kernel　partial　least　squares　(SENKPLS)　algorithm　that　suits　to　model　small　sample　high　dimensional　data　is　used　to　construct　a　feasibility-based　programming　(FBP)　model　with　the　true　training　samples;　then　prior　knowledge,　FBP　models　and　information　entropy　are　integrated　to　produce　virtual　training　samples.　Thirdly,　mutual　information　(MI)　method　is　used　to　select　the　spectral　features　of　the　new　mixing　training　samples　based　on　the　true　and　virtual　ones.　Finally,　a　soft　sensor　model　is　built　by　using　these　reduced　mixing　spectral　data.　Near-infra　spectra　data　and　mechanical　vibration　and　acoustic　singals　of　a　laboratory-scale　ball　mill　in　grinding　process　validate　the　reasonability　and　effectiveness　of　the　proposed　VSG　techniques　and　multi-component　mechanical　signals-based　modeling　approach.　Copyright　©　2018　Acta　Automatica　Sinica.　All　rights　reserved.