Research of Control Chart Pattern Recognition Based on Wavelet Decomposition in Lithium Battery Production
Pages 97 - 101
Abstract
Although the appearance of the control chart can help people visually observe the quality variation in the production process of lithium batteries, the impact of the variation on the battery quality cannot be directly known. How to use the data mining method to find out the relationship between the way of quality parameter variation and the final quality in the production process will have great practical significance for the production of lithium batteries and even the entire industrial production.
This paper uses one-dimensional discrete wavelet decomposition (DWT) combined with radial basis function (RBF) neural networks to identify patterns of process parameters' variation in a lithium battery production line, and then uses clustering method combined with mathematical statistics to find factors affecting battery quality. The research results provide theoretical models and empirical results of pattern recognition analysis for the production process of lithium batteries, and provide theoretical basis and analysis methods for other industrial production processes.
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- Research of Control Chart Pattern Recognition Based on Wavelet Decomposition in Lithium Battery Production
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Published In
May 2020
133 pages
ISBN:9781450375474
DOI:10.1145/3404687
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In-Cooperation
- Shenzhen University: Shenzhen University
- Sun Yat-Sen University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 July 2020
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Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICBDC 2020
ICBDC 2020: 2020 5th International Conference on Big Data and Computing
May 28 - 30, 2020
Chengdu, China
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