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Current fault classification generally depends on feature pattern difference of different fault classes.
This study intends to discover the local feature pattern difference between each bearing status and the healthy condition to characterize and discriminate different bearing statuses.
Process dynamics, control structure, nonlinear transformation, feature pattern extraction, and pattern-based prediction and its incorporation with a conventional linear controller are discussed.
Therefore, channel selection could be used to reduce the feature pattern size and lower the computational cost of feature extraction and classification.
Chang et al. [22] proposed a channel selection method to reduce the feature pattern size produced from Mirowski et al. work [20] for seizure prediction.
One control replicate was chosen as a reference dataset, based on a clear and representative feature pattern with minimum distortion.
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Open image in new window Fig. 5 Learned feature patterns.
A deviation from common topological feature patterns may be an indicator of anomaly.
Open image in new window Fig. 4 Original pattern of data Open image in new window Fig. 5 Typical learned feature patterns.
The use of the adapted FRFS allows the induction of low-dimensionality feature sets from feature patterns of a much higher dimensionality.
These types, introduced in the next section, form the basis for implementing random function generators, for defining powerful heuristics, and for guiding the search through feature patterns.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com