Solaris National Synchrotron Radiation Centre is a research facility that provides high quality synchrotron light. To control such a complex system it is necessary to monitor signals from various devices and subsystems. Despite the high demand for solutions to monitor the operation of centres, little work has concentrated on automatic analysis and fault detection. Anomaly detection prevents from financial loss, unplanned downtimes and in extreme cases cause damage. To address the problem a convolutional neural network (CNN) for fault detection in time series data has been proposed. The aim of the system is to identify abnormal status of sensors in certain time steps. In this study, we deploy transfer learning by examining pre-trained VGG-16, VGG-19, InceptionV3 and Xception CNN models with an adjusted densely-connected classifiers. Our database contains 336 h of signals in total which have been divided into 6300 time windows of 3 min length. The proposed solution, based on the VGG-16 architecture, detects anomalies in diagnostics signals with 92% accuracy and 85.5% precision what is a state-of-the-art result.

Solaris国家同步加速器辐射中心是提供高质量同步加速器光的研究机构。为了控制这样一个复杂的系统，有必要监视来自各种设备和子系统的信号。尽管对监控中心运行的解决方案有很高的要求，但很少有工作集中在自动分析和故障检测上。异常检测可防止经济损失，计划外停机以及在极端情况下造成损坏。为了解决该问题，提出了用于时间序列数据故障检测的卷积神经网络（CNN）。该系统的目的是在某些时间步长中识别传感器的异常状态。在这项研究中，我们通过使用调整后的密集连接分类器来检查预训练的VGG-16，VGG-19，InceptionV3和Xception CNN模型，从而部署转移学习。我们的数据库包含总共336小时的信号，这些信号已分为3分钟长度的6300个时间窗口。提出的解决方案基于VGG-16架构，以92％的准确度和85.5％的准确度检测诊断信号中的异常，这是最新的结果。