从不规则热图像序列中检测聚光太阳能电站的异常运行
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images
AI总结：
针对聚光太阳能电站热图像序列不规则、含日周期模式且高低温异常难区分的问题，提出基于深度序列预测的异常检测方法，性能优于多个SOTA基线并已落地部署。
摘要：
聚光太阳能（CSP）电站通过定日镜阵列将太阳光聚焦到中央塔顶的太阳能接收器上，加热储能介质来存储能量。这些接收器在高温下运行，面临结冰、变形和腐蚀等风险，可能导致运行故障、停机或代价高昂的设备损坏。我们研究了从一座在运CSP电站一年内收集的热图像序列中进行异常检测（AD）的问题。这些图像由安装在太阳能接收器上的红外摄像头在白天以1到5分钟不等的不规则间隔拍摄。我们的目标是开发一种方法，从高维热图像中提取有用的表征用于异常检测，该方法需要能够处理数据的时间特征，包括不规则性、图像间的时间依赖性，以及由强烈的日季节模式导致的非平稳性。与运行周期始末的正常图像相似的低温异常，与高温异常同时出现，构成了另一项挑战。我们首先评估了前沿的基于深度图像的异常检测方法，这些方法已被证明能有效提取有意义的图像表征以检测异常。随后，我们提出了一种基于预测的异常检测方法，该方法通过深度序列模型，根据过去的序列和时间戳预测未来的热图像。该方法有效捕捉了特定的时间数据特征，并能区分难以检测的基于温度的异常。我们的实验表明，在多个评估指标上，我们的方法相较于多个SOTA基线更具成效。我们还成功将解决方案部署在五个月的未见过的数据上，为CSP电站的维护提供了关键见解。我们的代码可在以下网址获取：https://tinyurl.com/ForecastAD
英文摘要：
Concentrated Solar Power (CSP) plants store energy by heating a storage medium with an array of mirrors that focus sunlight onto solar receivers atop a central tower. Operating at high temperatures these receivers face risks such as freezing, deformation, and corrosion, leading to operational failures, downtime, or costly equipment damage. We study the problem of anomaly detection (AD) in sequences of thermal images collected over a year from an operational CSP plant. These images are captured at irregular intervals ranging from one to five minutes throughout the day by infrared cameras mounted on solar receivers. Our goal is to develop a method to extract useful representations from high-dimensional thermal images for AD. It should be able to handle temporal features of the data, which include irregularity, temporal dependency between images and non-stationarity due to a strong daily seasonal pattern. The co-occurrence of low-temperature anomalies that resemble normal images from the start and the end of the operational cycle with high-temperature anomalies poses an additional challenge. We first evaluate state-of-the-art deep image-based AD methods, which have been shown to be effective in deriving meaningful image representations for the detection of anomalies. Then, we introduce a forecasting-based AD method that predicts future thermal images from past sequences and timestamps via a deep sequence model. This method effectively captures specific temporal data features and distinguishes between difficult-to-detect temperature-based anomalies. Our experiments demonstrate the effectiveness of our approach compared to multiple SOTA baselines across multiple evaluation metrics. We have also successfully deployed our solution on five months of unseen data, providing critical insights for the maintenance of the CSP plant. Our code is available at: https://tinyurl.com/ForecastAD
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
