Fault analysis of chemical equipment based on an improved hybrid model
Wu Huiyong, Kuan Jiang, Wang Yanyu
TL;DR
This paper introduces a new fault detection method for chemical equipment that combines advanced signal processing and deep learning to improve accuracy and reliability.
Contribution
The novel approach integrates VMD-LMS, asymmetric attention, and a pre-activation ResNet-BiGRU model for multimodal fault detection in chemical equipment.
Findings
The proposed method achieves a classification accuracy of 99.78% in fault detection.
It effectively handles non-stationary signals and complex temporal dependencies in chemical equipment data.
The model demonstrates strong generalization and robustness in noisy and diverse industrial environments.
Abstract
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…
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Taxonomy
TopicsFault Detection and Control Systems · Machine Fault Diagnosis Techniques · Engineering Diagnostics and Reliability