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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. 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