Signal Super Prediction and Rock Burst Precursor Recognition Framework Based on Guided Diffusion Model with Transformer
Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are as...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/6/3264 |
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| Summary: | Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning. |
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| ISSN: | 2076-3417 |