A gas leakage intelligent monitoring method based on multi-channel spatiotemporal perception and multi-view learning

Gas leak monitoring is a knowledge-intensive engineering task critical for maintaining mechanical integrity and service safety. In practical gas leakage scenarios, signals are weak and noisy, making accurate monitoring highly challenging. Existing methods cannot sufficiently capture the multi-channel spatiotemporal features and multi-view coupling relationships of signals under real operating conditions, resulting in low monitoring accuracy. Therefore, a multi-view spatiotemporal perception network (MSPN) based on multi-channel acoustic emission signals is proposed for adaptive gas leak monitoring. Specifically, a multi-channel screening strategy is first designed to identify reliable signal channels. Subsequently, the symmetric dot pattern (SDP) method is used to transform signals into image representations, thereby enhancing feature learnability. Next, a spatially enhanced Mamba (SEMamba) and a temporally enhanced bidirectional gated recurrent unit (TEBiGRU) are designed to form a multi-view feature extraction framework. The spatial features and temporal evolution patterns of multi-channel signals are captured by this framework. Finally, an adaptive optimization mechanism is designed to learn the optimal fusion weights of multi-view features during model training, enabling more effective integration of complementary spatial and temporal information for gas leakage monitoring. Experimental results on two measured gas leakage datasets demonstrate that the proposed method achieves high monitoring accuracy, outperforming fourteen state-of-the-art diagnostic approaches. The framework not only provides accurate and reliable signal-based diagnostics but also enables mechanical system health monitoring in industrial pipeline operations.