Under　strong　noise,　bearing　fault-related　instantaneous　frequency　(IF)　is　difficult　to　extract　by　time-frequency　analysis　(TFA)-based　ridge　extraction　method;　hence,　the　tacholess　order　tracking　is　unsuitable　for　characterizing　bearing　fault　characteristic　frequency　(FCF).　To　address　the　above　problem,　an　IF　estimation-based　order　tracking　is　developed　in　this　article.　The　fundamental　principle　of　the　developed　technique　is　to　obtain　the　IF　through　the　defined　instantaneous　frequency　estimation　operator　(IFEO)　and　recovery　factor,　and　then　the　initial　signal　is　resampled　using　the　IF　to　achieve　bearing　fault　diagnosis.　Specifically,　the　IFEO　is　first　defined　based　on　the　normalization　theory,　and　then　the　pseudo　signal　is　obtained　by　resampling　the　original　signal　through　the　IFEO　that　can　match　the　frequency-modulated　(FM)　law　of　the　original　signal.　Second,　the　spectra　concentration　index　is　constructed　to　calculate　the　optimal　IFEO.　Third,　the　recovery　factor　corresponding　to　the　optimal　IFEO　is　calculated　by　searching　the　highest　peak　from　the　envelope　spectrogram　of　the　pseudo　signal,　and　then　the　IF　of　the　maximum　amplitude　component　is　calculated.　Finally,　based　on　the　IF,　the　bearing　signal　is　resampled,　and　the　fault　characteristic　order　(FCO)　spectrum　is　obtained　to　detect　the　bearing　fault　type.　Analysis　results　of　the　simulated　and　measured　bearing　signals　indicate　that　the　developed　technique　can　accurately　predict　the　IF　and　detect　the　bearing　fault　and　has　better　effectiveness　in　calculating　IF　and　identifying　bearing　fault　type　than　the　traditional　ridge　extraction　method　under　strong　noise.