Data-driven　classification　models　have　gained　increasing　popularity　for　fault　detection　and　diagnosis　(FDD)　tasks　considering　their　advantages　in　implementation　flexibility　and　modeling　accuracies.　To　tackle　the　wide　existence　of　data　shortage　challenges　for　individual　buildings,　transfer　learning　can　be　adopted　to　enhance　the　applicability　of　data-driven　approaches.　At　present,　limited　studies　have　been　conducted　to　explore　the　potentials　of　transfer　learning　in　HVAC　FDD　tasks,　leaving　the　following　two　key　questions　unanswered,　i.e.,　(1)　whether　the　tabular　data　collected　from　different　building　systems　can　be　effectively　integrated　and　utilized　as　the　source　data　for　transfer　learning,　and　(2)　whether　the　operational　patterns　learnt　from　a　specific　building　system　can　be　interchangeably　applied　for　FDD　tasks　of　other　systems.　This　study　proposes　a　novel　image-based　transfer　learning　framework　to　tackle　the　multi-source　data　compatibility　challenge　in　the　building　field,　while　investigating　the　value　of　transfer　learning　in　cross-domain　FDD　tasks.　Data　experiments　have　been　designed　to　quantify　the　value　of　transfer　learning　given　different　data　amounts,　imbalance　ratios,　and　transfer　learning　strategies.　The　research　results　validate　the　usefulness　of　image-based　transfer　learning　for　HVAC　FDD　tasks.　The　insights　obtained　are　valuable　for　multi-source　building　operational　data　integration　and　cross-domain　knowledge　sharing.　©　2022　Elsevier　B.V.