Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

TL;DR

This paper presents an autoencoder-based iterative algorithm for detecting change-points and clustering subsequences in multivariate time-series data, improving robustness and performance over existing methods.

Contribution

The paper introduces a novel RNN autoencoder approach with a new curvature-based similarity measure for effective online and offline clustering of transient multivariate time-series.

Findings

Outperforms seven state-of-the-art algorithms in clustering accuracy.

Effectively detects change-points in complex multivariate data.

Enables robust condition monitoring in industrial systems.

Abstract

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.