1. The Usage of Hypothesis Testing in Identifying Anomalies in Time Series Data for Cigarette Cutting Production
- Author
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Qi Ji, Fanda Pan, Wei Ding, and Weiwen Zhang
- Subjects
Hypothesis testing ,deep learning ,cigarette cutting ,temporal convolutional network ,time series data ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
This work explores the application of hypothesis testing to enhance anomaly detection in time series data from the cigarette cutting production process, aiming to improve detection accuracy and robustness. Historical production data from the cigarette cutting workshop of the S Cigarette Factory are first collected. A deep learning-based anomaly detection model is developed using the Temporal Convolutional Network (TCN) algorithm, integrating TCN-Long Short-Term Memory (TCN-LSTM) and hypothesis testing. This model utilizes TCN-LSTM to extract features from the production data and applies the Wilcoxon signed-rank test for hypothesis testing. Results indicate that the model’s Mean Absolute Error, Mean Relative Square Error, and R2 values at a time step of 15 seconds are 11.54, 16.17, and 95.80%, respectively, improving by over 3% than other models. Additionally, the hypothesis testing further validates the model’s effectiveness and robustness at this optimal time step. Therefore, this model not only significantly enhances the accuracy of anomaly detection but also provides a new method and reference for intelligent monitoring and optimization of the cigarette cutting process.
- Published
- 2024
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