With　massive　construction　of　offshore　wind　turbines　(OWT),　structural　health　monitoring　(SHM)　techniques　have　been　applied　to　estimate　the　working　conditions　and　provide　early　warnings　of　structural　damage.　This　study　proposes　an　edge-computing-based　SHM　system　for　operational　modal　analysis　(OMA)　of　an　OWT.　ThssCe　system　comprised　an　open-source　data　acquisition　(DAQ)　device,　a　Raspberry　Pi　single-board　computer,　and　associated　drivers,　protocols,　and　algorithms.　The　DAQ　device　can　measure　the　acceleration　response　of　an　operating　OWT　subjected　to　environmental　excitation　and　transmit　raw　signals　to　the　Raspberry　Pi　according　to　the　UDP　pro-tocol.　The　data　are　further　processed　to　obtain　the　first-order　frequency　via　an　OMA　algorithm　developed　by　combining　Kalman　filtering,　the　random　decrement　technique　(RDT),　and　the　stochastic　subspace　identification　(SSI)　method.　The　proposed　SHM　system　was　implemented　in　a　scaled　OWT　model　to　verify　its　feasibility　for　field　use.　The　experimental　results　indicate　that　(1)　the　edge-computing-based　SHM　system　can　accurately　measure　the　response　of　the　OWT;　(2)　the　proposed　algorithm　can　estimate　the　modal　information　of　the　OWT　during　operation;　(3)　the　variation　in　the　first-order　frequency　induced　by　lateral　loads　can　be　identified　by　the　SHM　system.　Unlike　a　centralized　SHM　system,　the　proposed　edge-computing-based　SHM　system　does　not　require　massive　data　transmission,　making　it　promising　for　offshore　environments.