To　extract　the　magnetic　signal　generated　by　a　stress　concentration　of　buried　steel　pipeline　geomagnetic　environment　exactly,　and　to　overcome　the　drawback　of　the　existing　magnetic　testing,　which　lacks　effective　detection　of　high　sensitivity　multiple　probe　array　and　signal　processing　technology,　a　kind　of　method　using　magnetic　gradient　tensor　with　resonance　sparse　decomposition　and　bias　monostable　stochastic　resonance　(BMSR)　to　evaluate　the　pipeline　damage　identification　is　put　forward.　Firstly,　the　probe　arrangement　is　in　the　form　of　a　cross　tensor　array.　Secondly,　according　to　the　characteristics　of　the　pipeline　defect　and　on-site　interference　signals,　the　resonance　sparse　decomposition　of　the　signal　with　different　quality　factors　is　used　to　eliminate　some　interference　signals.　Finally,　the　stochastic　resonance　system　with　different　time　domain　recovery　is　added　with　quantum　genetic　algorithm　for　parameter　optimization.　The　identification　method　is　used　for　the　actual　station　pipeline　tensor　detection　signal,　compared　with　the　results　using　the　traditional　low-pass　filtering,　stochastic　resonance　system　combined　with　different　resonance　sparse　decomposition,　the　magnetic　gradient　tensor　resonance　sparse　decomposition　and　bias　monostable　processing　algorithm　is　verified　to　be　effective　on　extracting　pipeline　damage　characterization　of　magnetic　field　and　pipeline　stress　concentration.　©　2019,　Editorial　Department　of　JVMD.　All　right　reserved.