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Lynxsight: change-point detection through different distance-based common spatial patterns
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DOI：10.1007/s11227-025-07282-y.png)
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Abstract
En 中文
Detecting changes or shifts in data over time is known as change-point detection. This phenomenon is crucial for identifying the deterioration of industrial components and preventing costly breakdowns or failures. There are several supervised and unsupervised approaches used in change-point detection, which involve evaluating the difference between the sampling distributions of two-time windows. The accurate detection of change-points is a critical challenge addressed by Industry 4.0 and can enable timely action to avoid costly failures in industrial elements. This paper discusses the use of distance-based common spatial patterns (DB-CSPs) as an offline change-point detection technique in multivariate time series data. DB-CSP is a supervised approach that projects the data into a subspace to identify the most relevant features that differentiate between two-time windows. Afterward, a classification algorithm is used to effectively detect changes in the data. We demonstrate the adequacy of LynxSight using public datasets and apply the new approach to a real industrial use case, achieving better results than some state-of-the-art techniques.
Keywords:
Concept drift
Change-point detection
Common spatial pattern
Machine learning
Data streamming
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