Deep　learning　(DL)　based　diagnosis　models　have　to　be　trained　by　large　quantities　of　monitoring　data　of　machines.　However,　in　real-case　scenarios,　machines　operate　under　the　normal　condition　in　most　of　their　life　time　while　faults　seldom　happen.　Therefore,　though　massive　data　are　accessible,　most　are　data　of　the　normal　condition　while　fault　data　are　still　extremely　limited.　In　other　words,　fault　diagnosis　of　real　machines　is　actually　a　few-shot　diagnosis　problem.　To　deal　with　few-shot　diagnosis,　this　article　proposes　adaptive　knowledge　transfer　with　multiclassifier　ensemble　(AKTME)　under　the　paradigm　of　continual　machine　learning.　In　AKTME,　knowledge　learned　by　DL　models　is　considered　to　be　represented　by　the　learnable　filter　kernels　(FKs).　The　key　of　AKTME　is　a　proposed　continual　weighted　updating　(CWU)　technique　of　FKs.　By　CWU,　shared　FKs　are　distilled　from　multiple　auxiliary　tasks　and　adaptively　transferred　to　the　target　task.　Then　by　multiclassifier　ensemble,　AKTME　is　able　to　recognize　faults　with　few　fault　data　accessible.　AKTME　is　applied　on　two　few-shot　diagnosis　cases.　Results　verify　that　AKTME　achieves　higher　diagnosis　accuracies　than　recently　proposed　methods.　Moreover,　AKTME　tends　to　improve　the　diagnosis　accuracy　as　it　prelearns　on　more　auxiliary　tasks　continually.