Anomaly detection in large industrial cooling systems is very challenging due
to the high data dimensionality, inconsistent sensor recordings, and lack of
labels. The state of the art for automated anomaly detection in these systems
typically relies on expert knowledge and thresholds. However, data is viewed
isolated and complex, multivariate relationships are neglected. In this work,
we present an autoencoder based end-to-end workflow for anomaly detection
suitable for multivariate time series data in large industrial cooling systems,
including explained fault localization and root cause analysis based on expert
knowledge. We identify system failures using a threshold on the total
reconstruction error (autoencoder reconstruction error including all sensor
signals). For fault localization, we compute the individual reconstruction
error (autoencoder reconstruction error for each sensor signal) allowing us to
identify the signals that contribute most to the total reconstruction error.
Expert knowledge is provided via look-up table enabling root-cause analysis and
assignment to the affected subsystem. We demonstrated our findings in a cooling
system unit including 34 sensors over a 8-months time period using 4-fold cross
validation approaches and automatically created labels based on thresholds
provided by domain experts. Using 4-fold cross validation, we reached a
F1-score of 0.56, whereas the autoencoder results showed a higher consistency
score (CS of 0.92) compared to the automatically created labels (CS of 0.62) --
indicating that the anomaly is recognized in a very stable manner. The main
anomaly was found by the autoencoder and automatically created labels and was
also recorded in the log files. Further, the explained fault localization
highlighted the most affected component for the main anomaly in a very
consistent manner.

本研究基于自编码器综合考虑多变量时间序列数据中的异常检测及根本原因分析，通过总重构误差和局部重构误差判别冷却系统故障位置并结合专家知识进行根本原因分析，针对 34 个传感器 8 个月数据在冷却系统单元上进行了探究，并且对比验证结果表明自编码器方法能够稳定而且鲁棒性高地检测冷却系统中的异常。

基于自编码器的工业冷却系统异常检测和故障定位

Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

Reinforcement learning (RL) techniques have been developed to optimize
industrial cooling systems, offering substantial energy savings compared to
traditional heuristic policies. A major challenge in industrial control
involves learning behaviors that are feasible in the real world due to
machinery constraints. For example, certain actions can only be executed every
few hours while other actions can be taken more frequently. Without extensive
reward engineering and experimentation, an RL agent may not learn realistic
operation of machinery. To address this, we use hierarchical reinforcement
learning with multiple agents that control subsets of actions according to
their operation time scales. Our hierarchical approach achieves energy savings
over existing baselines while maintaining constraints such as operating
chillers within safe bounds in a simulated HVAC control environment.

通过使用分层强化学习技术，该论文对工业冷却系统进行优化，实现了节能的同时，控制了 HVAC 控制环境下冷却机的安全边界。