Intelligent Fault Diagnosis for Laboratory Equipment

Traditional laboratory management relies heavily on manual oversight and static threshold‐based fault diagnostics, which are inadequate for addressing the dynamic, individualised needs of contemporary environments. To address these challenges, we proposed an intelligent laboratory management framework, designed to enable secure and autonomous equipment monitoring. The system employed a hybrid CNN‐BiLSTM model with an attention mechanism to perform intelligent fault diagnosis. An improved empirical mode decomposition method enabled adaptive signal preprocessing. Experimental results demonstrate the proposed system's high accuracy, low latency, and robustness in real‐world laboratory scenarios. This approach presents a scalable and secure solution for intelligent laboratory management and fault detection.