The　maintenance　of　integrity　in　bolted　connections　remains　a　challenging　issue,　especially　when　subjected　to　external　disturbances　such　as　vibrations,　which　may　cause　extensive　or　localized　slip　at　the　interface　surfaces.　This　slip　phenomenon　exacerbates　the　relative　motion　between　interfaces,　leading　to　a　decrease　in　preload　levels,　i.　e.,　loosening　of　bolted　assemblies.　Over　the　past　decade,　detection　methods　such　as　vibration,　guided　waves　and　electromechanical　impedance　techniques　have　been　gradually　applied　for　detecting　loosening　in　bolted　connections.　With　the　significant　advancement　in　computational　capabilities,　machine　learning　algorithms　including　neural　networks　and　support　vector　machines　have　been　developed　to　further　enhance　the　accuracy　of　bolt　loosening　detection　methods.　The　integration　of　these　methods　offers　a　new　pathway　for　real-time　health　monitoring　of　bolted　connections.　This　paper　reviews　the　application　of　methods　based　on　the　acoustoelastic　effect,　vibration,　guided　waves,　electromechanical　impedance,　and　the　application　of　signal　analysis　methods　based　on　machine　learning　algorithms　in　the　field　of　bolt　connection　looseness　detection　and　monitoring,　aiming　to　showcase　the　research　progress　in　this　field　in　recent　years.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.