Incipient　faults　of　gears　and　rolling　bearings　in　rotating　machineries　are　very　difficult　to　identify　using　traditional　envelope　analysis　methods.　To　address　this　challenge,　this　paper　proposes　an　effective　local　spectrum　enhancement-based　diagnostic　method　that　can　identify　weak　fault　frequencies　in　the　original　complicated　raw　signals.　For　this　purpose,　a　traversal　frequency　band　segmentation　technique　is　first　proposed　for　dividing　the　raw　signal　into　a　series　of　subfrequency　bands.　Then,　the　proposed　synthetic　quantitative　index　is　constructed　for　selecting　the　most　informative　local　frequency　band　(ILFB)　containing　fault　features　from　the　divided　subfrequency　bands.　Furthermore,　an　improved　grasshopper　optimization　algorithm-based　stochastic　resonance　(SR)　system　is　developed　for　enhancing　weak　fault　features　contained　in　the　selected　most　ILFB　with　less　computation　cost.　Finally,　the　enhanced　weak　fault　frequencies　are　extracted　from　the　output　of　the　SR　system　using　a　common　spectrum　analysis.　Two　experiments　on　a　laboratory　planetary　gearbox　and　an　open　bearing　data　set　are　used　to　verify　the　effectuality　of　the　proposed　method.　The　diagnostic　results　demonstrate　that　the　proposed　method　can　identify　incipient　faults　of　gears　and　bearings　in　an　effective　and　accurate　manner.　Furthermore,　the　advantages　of　the　proposed　method　are　highlighted　by　comparison　with　other　methods.