Oil　pump　plays　an　important　role　in　the　field　of　oil　transportation,　while　high-speed　rotating　bearings,　as　important　parts　of　oil　pump,　often　suffer　various　forms　of　damage,　which　poses　a　certain　threat　to　the　safe　supply　of　oil　resources.　Based　on　the　experimental　data　of　bearings　from　Case　Western　Reserve　University,　ten　types　of　damage　data　were　selected　for　fault　identification　and　analysis.　Considering　the　influence　of　complex　working　conditions　of　the　station　on　the　collected　signals,　Gaussian　white　noise　was　added　to　the　experimental　data　to　get　close　to　the　collected　signals.　Based　on　the　energy　characteristics　of　the　data　obtained　by　wavelet　transform,　the　probabilistic　neural　network　is　used　to　classify　the　above　ten　kinds　of　feature　data.　The　results　show　that　the　accuracy　of　the　classification　of　the　proposed　model　is　99.76%,　which　is　much　higher　than　the　accuracy　of　the　current　common　models.　The　research　results　provide　a　reference　method　for　on-site　fault　identification　of　oil　pump　and　have　a　certain　engineering　practical　significance.　©　SPIE.