For　the　maintenance　of　power　lines,　a　core　task　is　to　diagnose　the　fault　of　different　components　from　the　aerial　inspection　images.　Currently,　deep-learning　models　trained　on　defect　samples　have　achieved　promising　performances　for　automatic　fault　diagnosis.　However,　the　slow　accumulation　process　of　fault　data　leads　to　a　long-term　challenge　of　data　insufficiency　in　this　field.　In　this　article,　a　normalized　multihierarchy　embedding　matching　(NMHEM)-based　anomaly　detection　method　is　proposed　to　inspect　power　line　faults,　which　only　utilizes　defect-free　samples　during　training.　To　impart　the　NMHEM　with　the　ability　to　detect　anomalous　patterns　in　images,　three　main　modules　are　introduced.　First,　the　embedding　generation　module　(EGM)　is　employed　to　extract　deep　hierarchical　representations.　Next,　hierarchy-wise　anomaly　scores　are　calculated　through　the　embedding　matching　module　(EMM)　to　measure　the　anomalous　degree,　which　can　make　the　model　more　discriminative　at　different　hierarchies.　Finally,　a　normalizing　module　(NM)　is　developed　and　served　as　a　credible　scoring　function　indicating　the　probability　of　anomaly　occurring,　thus　boosting　the　performance　of　the　fault　diagnosis.　The　proposed　NMHEM　adaptively　aggregates　local　spatial　and　global　semantic　information　which　leverages　the　available　nominal　knowledge　from　normal　data,　achieving　effective　fault　diagnosis　of　power　lines.　Experiments　are　conducted　on　the　dataset　that　contains　five　key　components.　Results　show　that　our　method　achieves　88.4%　area　under　the　curve　(AUC)　and　80.5%　F1-score,　which　outperforms　other　supervised　and　semisupervised　methods.