[论文解读] Fully Unsupervised Feature Alignment for Critical System Health Monitoring with Varied Operating Conditions

Gabriel Michau, Olga Fink|arXiv (Cornell University)|Jul 22, 2019

Fault Detection and Control Systems被引用 1

一句话总结

本文提出了一种完全无监督的特征对齐框架，以实现在多种运行条件下对关键安全工业系统进行早期健康监测。通过变分编码、空间关系保持和对抗性域判别，该方法在112座发电厂组成的机队中提升了单类分类性能，证明了在低故障环境中，无监督对齐对于实现稳健的早期故障检测至关重要。

ABSTRACT

The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained to be highly reliable and faults are rare. Fault detection can thus not be solved with supervised learning. Second, complex industrial systems usually have long lifetime during which they face very different operating conditions. In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model. In this paper, we propose a methodology to monitor the systems in their early life. To do so, we enhance the training dataset with other units from a fleet, for which longer observations are available. Since each unit has its own specificity, we propose to extract features made independent of their origin by three unsupervised feature alignment techniques. First, using a variational encoder, we impose a shared probabilistic encoder/decoder for both units. Second, we introduce a new loss designed to conserve inter-point spacial relationships between the input and the learned features. Last, we propose to train in an adversarial manner a discriminator on the origin of the features. Once aligned, the features are fed to a one-class classifier to monitor the health of the system. By exploring the different combinations of the proposed alignment strategies, and by testing them on a real case study, a fleet composed of 112 power plants operated in different geographical locations and under very different operating regimes, we demonstrate that this alignment is necessary and beneficial.

研究动机与目标

• 解决在故障罕见且数据有限的早期运行阶段监控关键安全系统所面临的挑战。
• 克服因机队中各机组运行条件不同而引起的域偏移问题。
• 在仅使用多台机组的正常运行数据、无需标注故障数据的情况下，实现对系统健康状态的鲁棒监控。
• 开发无监督特征对齐技术，使特征对齐于机组特异性特征，同时保持数据的内在结构。
• 在包含不同运行模式的112座真实发电厂组成的机队中，验证所提对齐策略的有效性。

提出的方法

• 使用变分自编码器在不同机组之间强制共享概率编码器/解码器，实现与机组来源无关的特征学习。
• 引入一种新型损失函数，以保持输入数据与学习到的特征之间的点间空间关系，从而维持局部数据结构。
• 训练一个对抗性判别器以识别特征的来源（即来自哪台机组），从而促使特征提取器生成域不变的表示。
• 将对齐后的特征输入单类分类器，基于正常运行模式检测异常。
• 在统一的训练框架中整合三种对齐组件——变分编码、空间损失和对抗训练。
• 通过在112座发电厂的真实数据上测试三种对齐策略的各种组合，对方法进行评估。

实验结果

研究问题

• RQ1 无监督特征对齐能否提升在故障数据有限的早期关键安全工业系统中的健康监测性能？
• RQ2 单独和组合使用的无监督对齐技术在减少不同运行条件下机组之间的域偏移方面效果如何？
• RQ3 在潜在空间中保持空间关系是否能增强异常检测的鲁棒性？
• RQ4 对特征来源进行对抗性训练在多大程度上促进了域不变表示学习？
• RQ5 所提出的对齐框架对于真实工业机队中的单类分类是否必要且有益？

主要发现

• 所提出的无监督特征对齐框架显著提升了在系统早期运行阶段进行健康监测的单类分类性能。
• 将三种对齐策略——变分编码、空间保持和对抗训练——全部结合使用，可获得最佳检测性能。
• 消融研究证实，每个对齐组件均有显著贡献，且三者结合对于在多样化运行模式下实现鲁棒性至关重要。
• 即使训练数据采集条件与未来运行状态不同，该方法仍能实现有效的系统监控。
• 该框架在涵盖不同地理位置和运行条件的112座真实发电厂中展现出实际应用价值。
• 结果表明，无监督对齐不仅有益，而且对于在低故障、高可靠性系统中实现可靠早期故障检测是必不可少的。