Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies
Abstract
1. Introduction
2. Problem Description
3. The Proposed Method
3.1. Determining the Optimal Pre-Warning Threshold
3.2. Extracting Features from an Historical Data Sequence
3.3. Generating Pre-Warnings by Combining the Mixture Entropies
3.4. Summary of the Proposed Method
Algorithm 1: Pre-warning for the remaining time to alarm |
4. Examples
4.1. Numerical Example A
4.2. Numerical Example B
4.3. Numerical Example C
4.4. Numerical Example D
4.5. Industrial Example
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
PLR | piecewise linear representation |
CNN–LSTM | convolutional neural network–long short-term memory |
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Yang, Z.; Wang, J.; Li, H.; Gao, S. Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies. Entropy 2025, 27, 736. https://doi.org/10.3390/e27070736
Yang Z, Wang J, Li H, Gao S. Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies. Entropy. 2025; 27(7):736. https://doi.org/10.3390/e27070736
Chicago/Turabian StyleYang, Zijiang, Jiandong Wang, Honghai Li, and Song Gao. 2025. "Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies" Entropy 27, no. 7: 736. https://doi.org/10.3390/e27070736
APA StyleYang, Z., Wang, J., Li, H., & Gao, S. (2025). Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies. Entropy, 27(7), 736. https://doi.org/10.3390/e27070736