The increasing automation in many areas of the Industry expressly demands to design efficient machine-learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g. shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by recommendation guidance for practitioners.
翻译:工业界许多领域日益自动化,明确要求设计高效的机器学习方法,以发现异常事件;随着传感器监测的部署无处不在,对复杂基础设施的健康状况的监测几乎是连续不断的,异常现象的探测现在可以依靠在非常高频取样的测量,对监测中的现象提供非常丰富的说明;为了充分利用所收集的信息,观察结果不再被视为多变数据,因此需要一种功能分析方法;因此,本文件的目的是调查在实际数据集功能设置中最近发现异常现象的技术的绩效;在对最新状态和视觉描述性研究进行概述之后,对各种异常现象的探测方法进行比较;虽然在功能设置中记录了异常现象的分类(例如形状、位置),但将所查明的异常情况指定一个具体类型似乎是一项具有挑战性的任务;因此,鉴于模拟研究中突出的这些类型,对现有方法的优缺点进行了基准。