As　a　representative　integrated　system　for　power-by-wire　(PBW)　systems,　Electro-hydrostatic　actuator　(EHA)　has　series　of　advantages　such　as　high　power　density,　compactness,　and　high　efficiency,　which　is　one　of　the　important　development　directions　of　future　hydraulic　system　field.　However,　due　to　its　high　integration　and　high　reliability　requirements,　it　is　challenging　to　conduct　degradation　studies　in　short　period　of　time　with　limited　data　samples.　For　this　type　of　high　integrated　mechatronics　system,　Prognostics　and　Health　Management　(PHM)　is　one　of　the　key　works　to　ensure　its　safety　and　reliability,　especially　the　performance　degradation　prediction　presented　in　this　paper.　To　deal　with　the　small　size　of　EHA　data,　a　time-based　data　enhancement　method　for　expanding　the　performance　data　set　is　proposed　based　on　Time　Generative　Adversarial　Network　(TimeGAN).　Considering　the　complex　of　working　state　and　system　performance,　the　relationship　between　the　EHA　operation　data　and　its　health　indicator　is　then　analyzed　using　the　CNN-BiLSTM-Attention　model,　so　as　to　generate　the　health　indicator　combine　with　TimeGAN　synthesis　data.　Finally,　CNN-BiLSTM-Attention　model　with　multi-input　channels　is　developed,　and　EHA　data　as　well　as　TimeGAN　synthesized　EHA　data　are　incorporated　into　the　model.　The　results　show　that　this　method　can　greatly　improve　the　prediction　accuracy　of　EHA　performance,　and　provide　a　novel　method　for　performance　degradation　prediction　of　integrated　mechatronic　system.　©　2024　Elsevier　Ltd