一种可迁移的自 logistic 模型，用于预测异构设备中的罕见故障
A Transferable Autologistic Model for Predicting Rare Failures in Heterogeneous Equipment
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中文总结 AI 辅助
本文针对异构设备罕见故障预测难题，提出可迁移自 logistic 模型，在含27台模拟冰箱的合成数据集上验证了模型的故障概率估计能力，为预测性维护提供支持。
中文摘要 AI 辅助
预测故障发生前的状态仍是预测性维护领域的重大挑战，尤其在故障罕见、同系列设备传感器配置不同、目标为故障预判而非确诊已观测故障的场景下。本文提出一种面向目标的概率模型，可学习同系列异构设备间共有的故障相关模式并简约适配目标设备；该模型明确考虑传感器异质性、运行环境与退化动态，生成适用于维护规划的校准故障概率估计。其性能在合成冰箱数据集上评估，该数据集包含27台模拟冰箱，具有不同的传感器配置、运行条件与故障类型，提供可控的实验环境。
英文摘要
Predicting failures before they occur remains a major challenge in predictive maintenance, particularly when failures are rare, when equipment of the same family differ in sensor configurations, and when the goal is anticipation rather than diagnosis of an already observed fault. This paper proposes a common-to-target probabilistic model that learns shared failure-related patterns across a family of heterogeneous equipment and adapts parsimoniously to target equipment. The model explicitly accounts for sensor heterogeneity, operating context, and degradation dynamics to produce calibrated failureprobability estimates suitable for maintenance planning. Its performance is evaluated on a synthetic refrigerator dataset comprising 27 simulated refrigerators with varying sensor configurations, operating conditions, and failure types, providing a controlle
发表机构
- Université de Sherbrooke（谢布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。
