用自编码器与聚类结合，自动识别水电站运行状态异常
Diagnostic Method for Hydropower Plant Condition-based Maintenance combining Autoencoder with Clustering Algorithms
- 先降维再聚类，发现数据中隐藏的运行模式
- 为每类状态训练自编码器，通过重建误差量化异常程度
- 适合需要智能诊断的工业设备运维人员参考
法国电力公司EDF利用监控系统和数据管理平台对水电站进行时序数据监测，但因监控站点的战略重要性不同，采集的时序数据量差异大，难以提取有效信息。为此，本文提出一种结合聚类算法与自编码神经网络的故障检测与诊断方法。首先使用降维算法生成二维或三维投影，帮助用户发现数据点间未被察觉的关系；随后采用多种聚类算法将数据点分组；针对每个聚类结果，训练一个自编码神经网络，在该簇内学习数据重构能力。通过计算各自编码器模型与实测值之间的重建误差，构建每个状态的接近度指标，实现对运行状态的智能识别与异常诊断。
原文摘要 · Abstract (English)
The French company EDF uses supervisory control and data acquisition systems in conjunction with a data management platform to monitor hydropower plant, allowing engineers and technicians to analyse the time-series collected. Depending on the strategic importance of the monitored hydropower plant, the number of time-series collected can vary greatly making it difficult to generate valuable information from the extracted data. In an attempt to provide an answer to this particular problem, a condition detection and diagnosis method combining clustering algorithms and autoencoder neural networks for pattern recognition has been developed and is presented in this paper. First, a dimension reduction algorithm is used to create a 2-or 3-dimensional projection that allows the users to identify unsuspected relationships between datapoints. Then, a collection of clustering algorithms regroups the datapoints into clusters. For each identified cluster, an autoencoder neural network is trained on the corresponding dataset. The aim is to measure the reconstruction error between each autoencoder model and the measured values, thus creating a proximity index for each state discovered during the clustering stage.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。