Control　rod　drive　mechanisms　(CRDMs),　as　critical　actuators　in　nuclear　power　plants,　regulate　control　rod　motion　by　manipulating　multiple　coil　currents　through　electromagnetic-mechanical　coupling.　Their　operational　integrity　is　vital　for　ensuring　reactor　safety.　However,　existing　anomaly　detection　approaches　predominantly　model　individual　coils,　neglecting　the　dynamic　interactions　among　coils　and　the　evolving　operational　patterns.　To　address　this　gap,　we　propose　a　multi-coil　joint　learning　(MCJL)　model　for　accurately　detecting　latent　anomalies　during　control　rod　operation.　In　this　approach,　coil　nodes　and　fully　connected　edges　are　defined,　and　a　decay　adjacency　matrix　is　introduced　to　construct　a　multi-coil　interaction　graph　that　captures　the　dynamic　coupling　among　the　lift,　moving,　and　solid　coils.　A　moving　graph　convolutional　network　is　then　employed　to　efficiently　extract　local　temporal　dependencies　across　coils　while　jointly　reconstructing　the　current　signals　of　all　three　coils.　The　residuals　between　the　reconstructed　and　actual　signals　are　subsequently　calculated,　enabling　per-cycle　anomaly　detection　using　a　multi-scale　dynamic　strategy.　Experimental　validation　using　historical　data　and　simulated　anomalies　from　a　pressurized　water　reactor　demonstrates　that　the　proposed　method　effectively　captures　dynamic　inter-coil　coupling　and　temporal　patterns,　achieving　high-precision　signal　reconstruction.　Compared　with　existing　methods,　MCJL　exhibits　superior　performance　in　both　reconstruction　and　anomaly　detection.　Furthermore,　its　dynamic　thresholding　strategy　provides　flexible　decision　boundaries　and　strong　fault　tolerance.　©　2025,　Science　Press.　All　rights　reserved.