Dual-impulse　behaviors　of　rolling　bearings　have　been　widely　researched　for　quantitative　diagnosis.　However,　it　is　challenging　to　accurately　extract　entry　and　exit　moments　of　the　fault　from　noise-contaminated　raw　signals.　To　address　this　issue,　a　novel　quantitative　diagnosis　method　based　on　digital　twin　model　is　proposed　to　assess　the　fault　severity　from　the　original　signal　waveform.　Specifically,　the　quantitative　diagnostic　criterion　for　bearing　faults　is　derived　to　reveal　the　instantaneous　response　characteristics　of　dual-impulse　behaviors,　and　then　a　digital　twin　model　is　constructed　to　characterize　the　fault　characteristics　of　the　measured　signal　with　noise-free　twin　signals.　Subsequently,　a　recursive　parameter　optimization　strategy　based　on　cosine　similarity　(RPOS-CS)　is　proposed　to　optimize　the　twin　model　in　real　time,　and　fault　parameters　of　the　optimal　signal　will　be　applied　to　evaluate　the　fault　size　of　the　bearing.　Finally,　kernel　density　estimation　is　employed　to　perform　uncertainty　analysis　on　multiple　diagnosis　results,　thereby　realizing　interval　estimation　and　significantly　enhancing　the　reliability　of　diagnosis　results.　Both　simulated　and　experimental　signals　are　utilized　to　validate　the　efficacy　of　the　proposed　method,　and　the　further　comparative　analysis　shows　that　it　exhibits　high　diagnostic　accuracy　and　outstanding　reliability.　©　2024　ISA