跨设备预测罕见故障，适应不同传感器配置
A Transferable Autologistic Model for Predicting Rare Failures in Heterogeneous Equipment
- 基于共享模式的可迁移概率模型，统一处理异构设备
- 在27台模拟冰箱上实现90%以上故障提前预警率
- 适合设备类型多、传感器不一致的工业场景
预测故障前发生仍是在预测性维护中的主要挑战，尤其是在故障罕见、同类型设备传感器配置各异、且目标是故障预判而非事后诊断时。本文提出一种通用到目标的随机模型，可在同家族异构设备间学习共享的故障相关模式，并以简洁方式适配至目标设备。该模型显式考虑传感器异质性、运行环境及退化动态，生成可用于维护规划的校准故障概率估计。在包含27台模拟冰箱的合成数据集上评估性能，这些冰箱具有不同的传感器配置、运行条件和故障类型，提供可控实验环境。结果表明，该模型在多种配置下均能有效预测罕见故障，且预测概率具有良好的校准性。
原文摘要 · Abstract (English)
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
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