The　degradation　of　high　voltage　circuit　breakers　is　the　result　of　the　interaction　between　the　equipment＇s　attributes　and　the　environmental　stress　such　as　the　electrical,　thermal　or　mechanical　stress.　Traditional　methods　of　degradation　analysis　often　rely　on　many　theoretical　assumptions　to　offer　the　detailed　relationship　between　the　machine＇s　condition　and　the　failure　probability.　However,　this　kind　of　theory　approximation　may　not　reflect　the　real　complicated　relationship.　Besides,　one　of　the　challenges　of　predicting　the　defect　distribution　lies　in　the　rich　kinds　of　different　defects.　The　sparsity　of　the　defect　kinds　on　a　single　circuit　breakers　makes　it　hard　to　establish　a　statistical　significance.　This　paper　introduces　an　data-driven　latent　Dirichlet　allocation　method　to　automatically　build　the　quantitative　relationship　between　the　individual　circuit　breaker　and　its　defect　distribution　on　many　kinds　of　defects.　Firstly,　a　latent　layer　is　introduced　between　the　circuit　breaker　and　its　defect　distribution　as　a　matrix　factorization　method.　The　defect　distribution　of　the　circuit　breakers　is　remodeled　as　the　distribution　between　the　circuit　breakers　and　the　latent　layers,　and　the　distribution　between　the　latent　layers　and　the　defects　based　on　Dirichlet　allocation,　which　greatly　reduced　the　state　space　to　build　a　reliable　discrete　distribution　on　each　kind　of　defect.　Second,　an　Bayesian　inference　is　also　introduced　as　an　online　extension　of　the　basic　method.　The　method　offers　a　new　way　to　analyze　large　amounts　of　log　data　in　the　power　grid,　and　has　the　ability　to　predict　the　defect　distribution　of　a　single　circuit　breaker.　The　experiment　shows　that　the　accumulated　probability　of　the　enhanced　LDA　method　is　23.1%　better　than　the　statistical　model　and　6.2%　better　than　the　LDA　model.　the　proposed　method　has　a　good　performance　of　defect　distribution　prognosis　and　can　offer　reasonable　operation　advice.