论文标题
与Kervolutional神经网络的时间序列中的异常检测和分类
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks
论文作者
论文摘要
最近，随着深度学习的发展，端到端的神经网络体系结构越来越多地应用于条件监测信号。他们已经证明了用于故障检测和分类的卓越性能，特别是使用卷积神经网络。最近，已经提出了卷积概念概念的概念，并在图像分类任务中提出了一些有希望的结果。在本文中，我们探讨了应用于时间序列数据的Kervolutional神经网络的潜力。我们证明，使用卷积和kervolutional层的混合物可以改善模型性能。混合模型首先应用于时间序列中的分类任务，作为基准数据集。随后，提出的混合体系结构用于检测由直升机上的加速度计记录的时间序列数据中的异常。我们建议使用时间自动编码器一种基于残差的异常检测方法。我们证明，将kervolutional与编码器中的卷积层混合对输入数据的变化更敏感，并且能够以更好的方式检测异常时间序列。
Recently, with the development of deep learning, end-to-end neural network architectures have been increasingly applied to condition monitoring signals. They have demonstrated superior performance for fault detection and classification, in particular using convolutional neural networks. Even more recently, an extension of the concept of convolution to the concept of kervolution has been proposed with some promising results in image classification tasks. In this paper, we explore the potential of kervolutional neural networks applied to time series data. We demonstrate that using a mixture of convolutional and kervolutional layers improves the model performance. The mixed model is first applied to a classification task in time series, as a benchmark dataset. Subsequently, the proposed mixed architecture is used to detect anomalies in time series data recorded by accelerometers on helicopters. We propose a residual-based anomaly detection approach using a temporal auto-encoder. We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.
