This　study　discusses　output　data　identification　algorithms　for　pipeline　structural　defects　using　independent　component　analysis;　a　powerful　tool　for　solving　blind　source　separation　(BSS)　problem.　Sparse　like　features　are　discovered　in　the　three-axis　magnetic　field　data　of　a　pipeline　use　to　be　the　hidden　targeted　Independent　sources　that　indicate　damage　information　of　the　structure　under　observation.　Wavelet　transform　algorithms　are　applied　on　the　3-axis　magnetic　field　data.　The　output　signals　are　cast　into　the　blind　source　separation　model　where　FastICA　algorithms　are　applied　on　the　waveletdomain　mixtures　to　separate　them　into　their　respective　independent　components.　Sharp　spikes　are　found　in　these　independent　components　that　clearly　show　the　time　instant　of　the　damage　occurrence.　The　location　of　the　pipeline　damage　can　be　found　by　exploiting　the　(time-based)　temporal　information　contained　in　the　spatial　signature　of　the　recovered　mixing　matrix.　WT-ICA　method　has　been　applied　on　synthetic　data　of　a　twelve　degree　of　freedom　time-varying　system　where　damage　is　modeled　by　abrupt　stiffness　variation.　Laboratory　experiments　and　real　world　underground　pipeline　data　were　recorded　and　fed　as　mixtures　in　to　the　WT-ICA　based　BSS　model.　Results　provide　clear　physical　interpretation　of　the　pipeline　structural　damages　subjected　to　various　kinds　of　stresses.　©　2018　IET　Conference　Publications.　All　rights　reserved.