In　view　of　misjudgment　of　unknown　new　faults　of　rolling　bearing　affects　bearing　safety　and　maintenance　efficienc,　a　fault　diagnosis　model　based　on　improved　gray　wolf　optimization　(GWO)　and　light　gradient　boosting　machine　(LightGBM)　was　proposed　to　realize　high　precision　discrimination　about　the　known　and　unknown　faults.The　time　domain,　frequency　domain　and　wavelet　domain　features　were　extracted　separately　from　the　vibration　signal　of　the　rolling　bearing　to　avoid　the　lack　of　feature　extraction　at　a　single　scale.The　GWOLightGBM　model　with　unknown　new　fault　diagnosis　mechanism　was　designed,　and　the　improved　gray　wolf　algorithm　with　Halton　sequence　and　simulated　annealing　strategy　was　constructed　to　realize　the　effective　optimization　of　model　parameters.The　experimental　results　showed　that　the　average　recognition　rate　of　the　model　for　known　and　unknown　faults　was　99.57%.The　average　recognition　rates　for　10　times　random　experiments　were　21.98%,　17.00%　and　9.27%　higher　than　logistic　regression　(LR),　Knearest　neighbor　(KNN)　and　support　vector　machine　(SVM),　respectively.The　comparative　experiments　verified　the　effectiveness　and　superiority　of　the　model,　which　can　identify　known　or　unknown　new　faults　with　high　accuracy.　©　2022,　Editorial　Department　of　Journal　of　Aerospace　Power.　All　right　reserved.