Firefighting Pump Anomaly Analysis in <scp>AIoT</scp> Maintenance Framework Based on <scp>IPSO</scp> and <scp>BiLSTM</scp> ‐ <scp>CNN</scp> Deep Learning Approaches

ABSTRACT Background The integration of Artificial Intelligence of Things (AIoT) has significantly advanced anomaly detection and predictive maintenance in industrial systems. Objective This study proposes an AIoT‐based framework for firefighting pump anomaly analysis using an Improved Particle Swarm Optimization (IPSO) and hybrid Bidirectional Long Short‐Term Memory–Convolutional Neural Network (BiLSTM‐CNN) model. Method IPSO is employed to optimize hyperparameters, enhancing the model's accuracy and convergence efficiency in detecting diverse failure modes. To evaluate robustness, the framework is tested under multi‐frequency sampling schemes with 30 s, 1 min, 5 min, and 10 min, analyzing the trade‐off between data granularity and computational cost. The proposed system is further validated through deployment in a real pump manufacturing company in Taiwan, demonstrating its practical applicability in industrial environments. The main contributions include: (i) an IPSO‐optimized BiLSTM‐CNN model for improved anomaly detection, (ii) a systematic multi‐frequency evaluation framework, and (iii) a real‐world AIoT deployment for predictive maintenance. Results Experimental results show that the proposed approach outperforms conventional models (RNN, CNN, GRU, LSTM, BiRNN, BiLSTM, and CNN‐BiLSTM), achieving up to 95.32% improvement in validation loss, 97.29% reduction in MSE, and over 6% gains in prediction and recall indices. Conclusions These results demonstrate the robustness and effectiveness of the proposed framework for reliable firefighting pump maintenance.