Aiming　at　that　the　local　tangent　space　alignment　(LTSA)　algorithm　could　not　use　samples＇　label　information　and　could　not　fast　process　incremental　dimension　reduction　problems,　a　new　incremental　supervised　local　tangent　space　alignment　(ISLTSA)　algorithm　was　proposed.　To　make　full　use　of　the　label　information　of　training　samples,　the　divergence　matrix　was　added　into　the　LTSA　algorithm　to　construct　a　new　minimum　objective　function.　The　lower　dimensions　were　made　to　embed　coordinates　for　homogeneous　clustering　and　heterogeneous　separating.　The　incremental　samples　might　affect　the　local　neighborhood　of　partial　training　samples.　Then　the　global　coordinate　matrix　was　updated　to　get　the　lower　dimension　coordinates　of　both　training　samples　and　the　incremental　ones,　the　lower　dimension　coordinates　were　taken　as　initial　values　to　do　eigenvalue　iteration　and　realize　updating　the　global　coordinates　of　all　samples.　Combined　with　the　classification　algorithm　of　support　vector　machine,　the　proposed　ISLTSA　algorithm　was　applied　in　gearbox　fault　diagnosis.　The　tests　verified　the　supervisory　and　learning　capacity　of　the　proposed　method,　it　was　shown　that　the　new　method　can　improve　the　fault　recognition　rate;　it　has　an　incremental　learning　ability,　and　can　reduce　the　complexity　of　the　dimension　reduction　method.　©　2018,　Editorial　Office　of　Journal　of　Vibration　and　Shock.　All　right　reserved.