The　fuel　pump　serves　as　the　central　component　of　the　aircraft　fuel　system,　necessitating　real-time　data　acquisition　for　monitoring　purposes.　As　the　number　of　sensors　increases,　there　is　a　substantial　rise　in　data　volume,　leading　to　a　simultaneous　increase　in　computational　processing　for　traditional　Prognostics　and　Health　Management　methods　while　computational　efficiency　decreases.　In　response　to　this　challenge,　a　novel　health　monitoring　approach　for　aircraft　fuel　pumps　is　proposed　based　on　the　collaborative　utilization　of　cloud-edge　resources.　This　approach　enables　efficient　cooperation　among　the　sensor　side,　edge　side,　and　cloud　side　to　achieve　timely　fault　warnings　and　accurate　fault　classification　for　fuel　pumps.　Within　this　method,　anomaly　judgment　tasks　are　allocated　to　the　edge　side,　and　an　anomaly　judgment　method　that　integrates　the　3　sigma　threshold　and　＂3/5　strategy＂　is　devised.　Additionally,　a　fault　diagnosis　algorithm,　founded　on　a　convolutional　auto-encoder,　is　formulated　in　the　cloud　to　discern　various　fault　types　and　severities.　Comparative　results　demonstrate　that,　in　contrast　to　long　short-term　memory　networks,　convolutional　neural　networks,　extreme　learning　machines,　and　support　vector　machines,　the　proposed　method　yields　improvements　in　accuracy　of　4.35%,　6.40%,　17.65%,　and　19.35%,　respectively.　Consequently,　it　is　evident　that　the　proposed　method　exhibits　notable　efficacy　in　the　condition　monitoring　of　aircraft　fuel　pumps.