A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating

摘要

The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different components related to ULEM, SLEM, and CEM were put together into what we refer to as TRANTOR: a conformal anomaly detection based industrial fleet monitoring framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units.