A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines

The segmentation of tunnel boring machine (TBM) time series plays a crucial role in analyzing TBM operating statuses and mining potential information from the collected signals. In this paper, a novel algorithm named SDP-RLR is proposed to segment multivariate TBM time series with outliers caused by harsh operating environments and changeable tunneling statuses. In this algorithm, the 3 sigma rule is extended to externally studentized residuals of a linear regression and used to identify the outliers in each segment of the input time series. An outlier removal penalty term is added to the segment errors to avoid regarding borderline data points as outliers. As a segmentation optimization algorithm, dynamic programming (DP) is improved to scalable dynamic programming (SDP) in this paper by combining time series in two stages to reduce computational costs. In the first stage, consecutive time points are integrated to reduce the calculations required for DP, while the second stage refines the segmentation results obtained from the first stage. Experiments are conducted on synthetic datasets to evaluate the performance of the SDP-RLR algorithm, and a complete TBM time series in terms of three key variables validates its effectiveness for segmentation and outlier detection tasks. The SDP-RLR algorithm can segment a TBM time series into four different statuses, which assists in analyzing the operating statuses of TBMs. In addition, comparative experiments indicate that the proposed algorithm can handle the segmentation of complex TBM time series when they cannot be successfully processed using the segmentation approach after outlier detection. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a novel algorithm named SDP-RLR is proposed to segment multivariate TBM time series with outliers caused by harsh operating environments and changeable tunneling statuses. The algorithm combines the 3 sigma rule with linear regression and dynamic programming to identify and remove outliers, and achieves efficient segmentation of TBM time series.