发表机构
CentraleSupélec, Université Paris-Saclay（巴黎中央理工-高等电力学院，巴黎萨克雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CORD通过联合预训练跨异构物理系统学习可复用退化表征，结合类型特定接口与共享主干，在轴承、电池等数据集上优于单域模型，并改善跨单元生命周期一致性。
AI 中文摘要
异构物理退化系统能否受益于联合预训练，并超越特定系统的预测性维护，迈向可复用的跨系统表征学习？CORD将类型特定的观测接口与共享的退化主干网络相结合。其两个自监督目标在互补的尺度上学习：观测内结构建模（ISM）捕获观测内部的结构，而观测间动态建模（IDM）捕获跨观测历史的潜在退化演化。我们在两种迁移边界下评估CORD：预训练包含的系统类型，其中下游数据集和保留单元是未见过的，但其系统类型在源预训练期间已有表示；以及预训练排除的系统类型，其中整个涡扇发动机类型在预训练中缺失。在轴承、电池和切削刀具上，CORD（多域）在冻结适应下始终优于CORD（单域），在大多数设置的全量微调下提供进一步改进，并与代表性的外部基线保持竞争力。源预训练初始化还改善了针对预训练排除的发动机类型的低标签适应。冻结表征分析进一步表明，多域预训练后跨单元生命周期一致性得到改善。因此，跨异构物理系统的联合预训练产生了可在设备、数据集和系统类型之间复用的退化表征。
英文摘要
Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system representation learning? CORD combines type-specific observation interfaces with a shared degradation backbone. Its two self-supervised objectives learn at complementary scales: Intra-Observation Structure Modeling (ISM) captures structure within observations, while Inter-Observation Dynamics Modeling (IDM) captures latent degradation evolution across observation histories. We evaluate CORD under two transfer boundaries: Pretraining-Included System Types, where downstream datasets and held-out units are unseen but their system types are represented during source pretraining, and Pretraining-Excluded System Types, where the entire turbofan-engine type is absent from pretraining. Across bearings, batteries, and cutting tools, CORD (Multi-domain) consistently improves over CORD (Single-domain) under Frozen adaptation, provides further gains under Full FT in most settings, and remains competitive with representative external baselines. Source-pretrained initialization also improves low-label adaptation to the pretraining-excluded engine type. Frozen-representation analysis further shows improved cross-unit lifecycle consistency after multi-domain pretraining. Joint pretraining across heterogeneous physical systems thus produces degradation representations reusable across devices, datasets, and system types.
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