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The safety and reliability of chemical equipment are crucial to industrial production, as they directly impact production efficiency, environmental protection, and personnel safety. However, traditional fault detection techniques often exhibit limitations when applied to the complex operational conditions, varying environmental factors, and multimodal data encountered in chemical equipment. These conventional methods typically rely on a single signal source or shallow feature extraction, which makes it difficult to effectively capture the deep, implicit information within the equipment’s operating state. Moreover, their accuracy and robustness are easily compromised when confronted with noisy signals or large, diverse datasets. Therefore, designing an intelligent fault detection method that integrates multimodal data, efficiently extracts deep features, and demonstrates strong generalization capability has become a key challenge in current research.This paper proposes an innovative fault detection method for chemical equipment aimed at improving detection accuracy and efficiency, providing technical support for intelligent and predictive maintenance. The method combines Variational Mode Decomposition (VMD), Least Mean Squares (LMS) processing, an asymmetric attention mechanism, and a pre-activation ResNet-BiGRU model to create an efficient framework for multimodal data fusion and analysis. First, the VMD-LMS process handles complex non-stationary signals, addressing the issue of mode mixing. Next, an asymmetric attention mechanism optimizes the ResNet, enhancing feature representation capabilities through deep learning. The pre-activation mechanism introduced in the residual blocks of ResNet improves training efficiency and model stability. Subsequently, the BiGRU model is used to model the extracted features in the time domain, capturing complex temporal dependencies. Experimental results demonstrate that the proposed method performs exceptionally well in chemical equipment fault detection, significantly enhancing diagnostic timeliness and reliability, achieving a classification accuracy of 99.78%, and providing an effective fault detection solution for industrial production.