The　slewing　bearing　is　a　critical　component　in　large　equipment　like　shield　machines　and　wind　turbines.　Because　slewing　bearings　operate　in　complex　situations　with　fluctuating　speed　and　load　on　a　regular　basis,　the　vibration　signal　they　produce　contains　several　interferences,　making　fault　features　difficult　to　identify.　The　specific　objective　of　this　study　is　to　provide　a　new　fault　diagnosis　method,　named　difference　optimization　bispectrum,　for　slewing　bearing　signals　under　strong　noise　interference.　The　method　designs　a　convex　optimization　bispectrum　model　by　the　convex　optimization　theory,　covering　the　shortage　of　traditional　decomposition　by　differentiating　features.　Based　on　the　model,　a　two-dimensional　weight　coefficient　is　constructed　to　calculate　the　difference　optimization　bispectrum,　which　reduces　the　noise　and　enhances　the　features　in　positive　and　negative　bispectrumdomain.　This　study　offers　a　fresh　perspective　on　extraction　of　fault　information　from　the　signal　under　strong　noise　interference,　making　an　original　contribution　for　the　fault　diagnosis　of　the　slewing　bearing.　The　experiment　work　presented　here　provides　the　practical　effect　of　the　method　for　the　slewing　bearing　signals.