The　bogie　system　is　known　as　the　＂legs＂　of　high-speed　train　(HST),　various　failures　will　inevitably　occur　under　large　disturbances,　high　speeds,　and　heavy　loads.　Abnormal　detection　(AD)　is　an　important　means　to　detect　the　health　status　of　its　key　components.　Nevertheless,　the　cross-correlation　of　failures　causes　the　confusion　of　health　data　and　fault　data　under　highly-coupled　components,　which　leads　to　the　issue　of　false　detection　and　missing　detection.　Hence,　this　paper　proposes　a　dual　convolutional　autoencoder　(d-CAE)　network　combined　with　unsupervised　domain-adversarial　learning　for　group　anomaly　detection　of　bogies.　Firstly,　the　d-CAE　adopts　temporal　window　aggregation　to　construct　initial　inputs.　Afterwards,　the　domain-adversarial　learning　strategy　is　utilized　to　make　the　d-CAE　realize　multi-level　encoding　and　reconstruction　of　multi-channel　time-series.　Finally,　a　parameterized　dynamic　AD　index　is　designed　to　accurately　establish　the　health　sample　guided　abnormal　decision　boundary.　The　experimental　results　indicate　that　the　d-CAE　is　competitive　in　the　aspects　of　detection　accuracy　and　robustness　compared　with　the　state-of-the-art　methods.　©　2025　Institute　of　Physics　Publishing.　All　rights　reserved.