Martí Luis, Sanchez-Pi Nayat, Molina José Manuel, Garcia Ana Cristina Bicharra
Department of Electrical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil.
Instituto de Lógica, Filosofia e Teoria da Ciéncia (ILTC), Niterói 24020-042, Brazil.
Sensors (Basel). 2015 Jan 27;15(2):2774-97. doi: 10.3390/s150202774.
Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
异常检测是指在数据中寻找不符合先验预期行为的模式的问题。这与某些样本在给定度量标准下与数据集的其他部分距离较远的问题相关，这些异常样本被视为离群值。由于异常检测在诸如入侵检测、欺诈检测、故障检测和系统健康监测等众多实际应用中的相关性，最近它吸引了研究界的关注。异常本身根据其上下文和解释可能具有正面或负面性质。然而，在任何一种情况下，决策者能够检测到它们以便采取适当行动都很重要。石油行业是存在这些问题的应用场景之一。正确检测此类异常信息使决策者有能力对系统采取行动，以便正确避免、纠正或应对与之相关的情况。在该应用场景中，用于泵送和发电操作的重型提取机器，如涡轮机，由数百个传感器密集监测，每个传感器都高频发送测量数据以预防损坏。在本文中，我们提出将另一种分割算法（YASA，一种新颖的快速且高质量的分割算法）与一类支持向量机方法相结合，用于涡轮机中的高效异常检测。该提议旨在处理上述任务并应对标记训练数据的缺乏。结果，我们进行了一系列实证研究，将我们的方法与应用于基准问题和与石油平台涡轮机异常检测相关的实际应用的其他方法进行比较。
