Air　handling　units　have　been　widely　adopted　in　modern　buildings　for　indoor　air　regulation　and　circulation　tasks.　The　accurate　and　reliable　fault　detection　and　diagnosis　(FDD)　of　air　handling　units　(AHU)　are　of　great　significance　to　maintain　indoor　environment　while　ensuring　the　energy　efficiency　in　building　operations.　Data-driven　FDD　approaches　have　gained　great　popularity　due　to　their　excellence　and　flexibilities　for　practical　applications.　Given　sufficient　labeled　data,　existing　studies　have　validated　the　value　of　various　supervised　learning　algorithms　for　FDD　tasks.　However,　it　can　be　very　challenging,　expensive,　time-consuming　and　labor-intensive　to　obtain　data　labels　for　faulty　operations,　making　it　impractical　to　fully　realize　the　potential　of　advanced　supervised　learning　algorithms.　To　tackle　this　problem,　this　study　proposes　a　novel　semi-supervised　FDD　method　using　neural　networks.　The　method　adopts　the　self-training　strategy　for　semi-supervised　learning　and　has　been　tested　for　two　practical　applications,　i.e.,　fault　diagnosis　and　unseen　fault　detection.　A　number　of　data　experiments　have　been　conducted　to　statistically　characterize　the　influence　of　key　learning　parameters,　including　the　labeled　data　availabilities,　the　maximum　semi-supervised　learning　iterations,　the　threshold　and　learning　rate　for　pseudo-label　data　utilization.　The　results　indicate　that　the　method　can　effectively　enhance　model　generalization　performance　by　utilizing　large　amounts　of　unlabeled　data.　The　insights　obtained　are　helpful　for　developing　advanced　data-driven　tools　for　smart　building　system　fault　detection　and　diagnosis.　(C)　2021　Elsevier　B.V.　All　rights　reserved.