Adaptive Noise Reduction Pipeline Leakage Detection Method Based on Multimodal Transformer and Contrastive Learning

Under the dual constraints of strong industrial noise and small sample size, achieving high precision and reliability in pipeline leakage detection is a significant challenge. The root cause lies in the fact that traditional single-modal methods have a single perception dimension and insufficient anti-interference ability, making it difficult to effectively distinguish strong background noise with overlapping spectra, resulting in reduced measurement accuracy and reliability. To address this issue, this paper proposes an adaptive noise reduction detection method based on multi-modal Transformer and contrastive learning (MDT-CL). This paper uses pressure, vibration, and acoustic emission sensors to capture complementary signals, and synchronizes the collection of these signals through dynamic time warping technology. The proposed method comprises three key innovations. Firstly, we propose a multi-scale Transformer encoder based on physical principles, employing an asymmetric cross-modal attention mechanism to focus on pressure signals that are sensitive to leakage. We integrate vibration and acoustic features and fuse them at the feature level, fundamentally solving the heterogeneity and spatio-temporal mismatch problems of multi-source asynchronous sensor data. Secondly, we propose an embedded adaptive spectral normalization module, which is a dynamic frequency-domain filtering technology that collaboratively optimizes with the network. It intelligently suppresses noise-sensitive frequency bands and synchronously enhances leakage features, effectively overcoming the limitations of traditional static filtering. Thirdly, we propose a contrastive learning pre-training strategy for pipeline noise. By constructing a multi-modal noise enhancement library and feature decoupling mechanism, it significantly expands the inter-class distance between leakage and normal operating condition noise, enhancing the model’s generalization ability in complex noise conditions. Experimental results demonstrate that the proposed method achieves a leakage detection accuracy of 96.8%, representing a 1.6% improvement over the current state-of-the-art method.