Abstract Concept drift refers to the inevitable phenomenon that influences the statistical
features of the data stream. Detecting concept drift in data streams quickly and precisely …

Concept drift arises from the uncertainty of data distribution over time and is common in data
stream. While numerous methods have been developed to assist machine learning models …

Accurately sensing the main bearing state and diagnosing fault types is crucial to ensure the
safe operation of the main drive system of tunnel boring machines. Currently, the research …

In this paper, we propose an efficient segmentation approach in order to divide a
multivariate time series through integrating principal component analysis (PCA), visibility …

Surrounding rock pressure prediction for fully mechanized coal face (FMCF) roof
management and control is of great significance. The key challenge is to effectively combine …

During subway tunnel construction, the consumed time for each ring (unit of construction
progress) is highly dependent on objective factors such as geological conditions and shield …

The operation data of a tunnel boring machine (TBM) reflects its geological conditions and
working status, which can provide critical references and essential information for TBM …

Error-bounded piecewise linear approximation (l∞-PLA) has been proven effective in
addressing challenges in data management and analytics. It works by approximating the …

Due to the challenging and unpredictable construction environment, as well as the intricate
structure and operational mechanisms of the equipment, the in-situ data acquired from …

Turnout equipment is a key component to ensure the safe operation of trains. How to identify
turnout faults is one of the important tasks of railway engineering departments and electrical …