Time-Frequency Analysis of Hot Rolling Using Manifold Learning
Abstract
In this paper, we propose a method to compare and visualize spectrograms in a low dimensional space using manifold learning. This approach is divided in two steps: a data processing and dimensionality reduction stage and a feature extraction and a visualization stage. The procedure is applied on different types of data from a hot rolling process, with the aim to detect chatter. Results obtained suggest future developments and applications in hot rolling and other industrial processes.
Domains
Computer Science [cs]| Origin | Files produced by the author(s) |
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