Interpretable Event-Driven Multisensor Risk-Evolution Analysis for Methane Early Warning

Area of Science:

• Mining Engineering
• Data Science
• Risk Management

Background:

• Methane exceedance events in underground coal mines are complex, involving multiple subsystems.
• Current research often lacks interpretability in predicting abnormal evolution patterns leading to methane exceedance.

Purpose of the Study:

• To develop an interpretable, event-driven multisensor risk-evolution analysis framework for methane early warning.
• To provide insights into the emergence of abnormal evolution patterns preceding methane exceedance.

Main Methods:

• Extraction of methane exceedance events from multisensor data.
• Development of a continuous multisensor risk representation and persistence-based trigger.
• Construction of event-specific temporal dependency networks using lagged dependency analysis.

Main Results:

• The proposed framework achieved superior early-warning performance on a real-world dataset.
• An effective warning rate of 0.848 was attained under the Any-Target-Sensor criterion.
• The framework significantly outperformed benchmark methods (p < 0.001).

Conclusions:

• Methane exceedance events are linked to structured multisensor abnormal evolution, not just isolated fluctuations.
• The framework offers an interpretable, system-level perspective for methane early warning.
• This approach enhances safety by providing timely and understandable risk assessments.