The　long-term　operating　structural　health　monitoring　(SHM)　system　generates　massive　monitoring　data,　whose　transmission　and　storage　are　challenging　tasks.　To　address　this　issue,　this　study　proposed　an　improved　grey　wolf　optimizer-orthogonal　matching　pursuit　(IGWO-OMP)　algorithm　for　SHM　data　compression　and　reconstruction.　The　effectiveness　of　the　proposed　algorithm　was　validated　using　four　SHM　datasets　including　ultrasonic　guided　wave　(UGW),　acceleration,　and　audio　signals.　Firstly,　the　optimization　results　of　IGWO　were　compared　with　those　of　other　classical　algorithms　to　verify　its　superiority　in　reconstruction　accuracy.　Secondly,　the　reconstruction　accuracy　under　various　compression　factors　was　investigated　and　the　optimal　compression　factor　was　determined.　Finally,　the　relationship　between　reconstruction　accuracy　and　signal　sparsity　as　well　as　the　performance　of　IGWO-OMP　in　dealing　with　noise-containing　signals　were　discussed.　The　results　demonstrate　the　generalizability　and　excellent　noise　robustness　of　IGWO-OMP　in　SHM　data　compression　and　reconstruction.　Compared　with　other　optimization　algorithms,　IGWO　exhibits　the　best　global　optimal　solution　searching　ability　and　obtains　the　reconstructed　signal　closest　to　the　original　one.　Comprehensively　considering　the　compressed　signal　length,　reconstruction　accuracy　and　computational　efficiency,　the　optimal　compression　factor　is　suggested　to　be　0.2.　In　IGWO-OMP,　the　signal　reconstruction　accuracy　is　related　to　its　sparsity,　the　variation　of　the　CCD　index　for　the　reconstructed　signal　with　relative　sparsity　follows　an　exponential　growth　relationship.　©　2024　Elsevier　Ltd