This paper introduces an algorithm for the detection of change-points and the
identification of the corresponding subsequences in transient multivariate
time-series data (MTSD). The analysis of such data has become more and more
important due to the increase of availability in many industrial fields.
Labeling, sorting or filtering highly transient measurement data for training
condition based maintenance (CbM) models is cumbersome and error-prone. For
some applications it can be sufficient to filter measurements by simple
thresholds or finding change-points based on changes in mean value and
variation. But a robust diagnosis of a component within a component group for
example, which has a complex non-linear correlation between multiple sensor
values, a simple approach would not be feasible. No meaningful and coherent
measurement data which could be used for training a CbM model would emerge.
Therefore, we introduce an algorithm which uses a recurrent neural network
(RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The
scoring function uses the reconstruction error and latent space information. A
model of the identified subsequence is saved and used for recognition of
repeating subsequences as well as fast offline clustering. For evaluation, we
propose a new similarity measure based on the curvature for a more intuitive
time-series subsequence clustering metric. A comparison with seven other
state-of-the-art algorithms and eight datasets shows the capability and the
increased performance of our algorithm to cluster MTSD online and offline in
conjunction with mechatronic systems.

本文介绍了一种用于检测变点和识别瞬态多变量时间序列数据中相应子序列的算法，该算法使用基于递归神经网络 (RNN) 的自编码器 (AE)，通过重构误差和潜在空间信息来建立子序列模型，提出一种基于曲率的相似性度量进行更直观的时间序列子序列聚类，通过与其他七种算法的对比实验表明，我们的算法可以更好地在线及离线聚类多变量时间序列数据。