论文标题
使用两阶段自动编码器的大规模流体处理厂上多元时间序列的异常检测
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder
论文作者
论文摘要
本文着重于具有动态成分的大规模流体处理植物中多变量时间序列数据的异常检测，例如发电，水处理和化学植物，同时观察到来自各种物理现象的信号。在这些工厂中，鉴于熟练的工程师数量的减少和人力短缺，对降低运营和维护成本的需求正在增加，以降低运营和维护成本。但是，考虑到高维信号的复杂行为以及对解释性的需求，这些技术构成了一个重大挑战。我们将两阶段的自动编码器（TSAE）作为适合此类植物的异常检测方法。这是一个简单的自动编码器架构，使异常检测更加容易解释和更准确，在基于植物信号可以分为几乎与彼此无关的两种行为的前提下，这些信号被分为长期和短期组成部分，以逐步的方式分为逐步培训的组件，并以独立培训的独立能力来提高标志性的标志性。通过对两个公开可用水处理系统数据集的实验，我们已经确认了高检测性能，前提的有效性，并且模型行为是按预期的，即TSAE的技术有效性。
This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical plants, where signals from various physical phenomena are observed simultaneously. In these plants, the need for anomaly detection techniques is increasing in order to reduce the cost of operation and maintenance, in view of a decline in the number of skilled engineers and a shortage of manpower. However, considering the complex behavior of high-dimensional signals and the demand for interpretability, the techniques constitute a major challenge. We introduce a Two-Stage AutoEncoder (TSAE) as an anomaly detection method suitable for such plants. This is a simple autoencoder architecture that makes anomaly detection more interpretable and more accurate, in which based on the premise that plant signals can be separated into two behaviors that have almost no correlation with each other, the signals are separated into long-term and short-term components in a stepwise manner, and the two components are trained independently to improve the inference capability for normal signals. Through experiments on two publicly available datasets of water treatment systems, we have confirmed the high detection performance, the validity of the premise, and that the model behavior was as intended, i.e., the technical effectiveness of TSAE.
