Owing　to　high　transmission　ratio　and　compact　structure,　planetary　gearboxes　are　widely　used　in　industrial　applications.　However,　due　to　running　under　noisy　environment　and　time-varying　rotational　speed　conditions,　the　acquired　vibration　signals　are　nonstationary,　which　seriously　degrade　the　performance　of　fault　diagnosis　methods　of　planetary　gears.　To　address　this　problem,　a　fault　diagnosis　method　of　planetary　gears　using　a　stacked　denoising　autoencoder　(SDAE)　and　a　gated　recurrent　unit　neural　network　(GRUNN)　is　proposed　in　this　paper.　First,　a　hybrid　model　based　on　SDAE　and　GRUNN　is　developed　to　remove　noise　components　from　input　data　and　process　pre-　and　post-correlation　time-series　data.　The　training　samples　for　planetary　gear　fault　diagnosis　are　regarded　as　the　input　data　of　the　developed　hybrid　model　to　automatically　extract　robust　fault　features.　Then,　the　training　process　of　the　developed　hybrid　model　is　presented.　The　Adam　optimization　algorithm　is　utilized　to　optimize　the　parameters,　and　the　dropout　technique　is　employed　to　prevent　from　overfitting.　Finally,　a　softmax　classifier　is　employed　to　identify　planetary　gear　states　in　test　samples.　The　effectiveness　of　the　proposed　method　is　validated　through　a　fault　diagnosis　experiment　of　planetary　gears.　The　experimental　results　show　that　the　proposed　method　possesses　strong　anti-noise　ability　and　adaptability　to　time-varying　rotational　speed.