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AIoT Fault Detection for Firefighting Pump Maintenance Services Based Metaheuristics and Combined Deep Learning Methodologies

AIoT Fault Detection for Firefighting Pump Maintenance Services Based Metaheuristics and Combined... Firefighting pumps are vital components in fire safety systems, and their proper maintenance is essential for operational reliability. Conventional maintenance methods significantly depend on manual inspection and labor‐intensive procedures, which are time‐consuming and require significant personnel and capital expenses, particularly in large infrastructures. This paper introduces a novel fault detection framework leveraging artificial intelligence of things (AIoT) technology to enhance firefighting pump maintenance services. An advanced hybrid deep learning approach, IPSO‐GRU‐CNN, is developed to improve failure classification accuracy. The improved particle swarm optimization (IPSO) methodology is employed for hyperparameter optimization of the gated recurrent unit and convolutional neural network (GRU‐CNN) model, demonstrating superior performance to conventional optimization methods such as PSO and random search. The IPSO‐GRU‐CNN model is extensively compared with various deep learning architectures, including recurrent neural networks (RNN), CNN, long short‐term memory (LSTM), GRU, and CNN‐GRU, to assess its classification accuracy and efficiency. The suggested AIoT framework optimizes the fault detection process and demonstrates a practical and scalable solution for industrial applications, significantly reducing labor costs and capital expenses associated with the maintenance services of firefighting pumps. Experimental results demonstrated that the developed framework outperforms conventional techniques in terms of classification accuracy and error. Comparing across conventional techniques, IPSO‐GRU‐CNNs acquire the most significant enhancements of 73.37% loss, 98.88% validating loss, 25.84% CP, 89.72% validating CP, 74.64% MAE, 97.36% validating MAE, 74.21% MSE, 99.9% validating MSE, 5.8% PRE, 5.78% validating PRE, 5.06% REC, and 5.2% validating REC. This framework offers a robust and efficient solution for predictive maintenance in firefighting pump systems, facilitating early fault detection and reducing downtime. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Intelligence Wiley

AIoT Fault Detection for Firefighting Pump Maintenance Services Based Metaheuristics and Combined Deep Learning Methodologies

Nguyen, Thanh‐Phuong
Computational Intelligence , Volume 41 (3) – Jun 1, 2025
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References (55)

Publisher
Wiley
Copyright
© 2025 Wiley Periodicals LLC.
eISSN
1467-8640
DOI
10.1111/coin.70071
Publisher site
See Article on Publisher Site

Abstract

Firefighting pumps are vital components in fire safety systems, and their proper maintenance is essential for operational reliability. Conventional maintenance methods significantly depend on manual inspection and labor‐intensive procedures, which are time‐consuming and require significant personnel and capital expenses, particularly in large infrastructures. This paper introduces a novel fault detection framework leveraging artificial intelligence of things (AIoT) technology to enhance firefighting pump maintenance services. An advanced hybrid deep learning approach, IPSO‐GRU‐CNN, is developed to improve failure classification accuracy. The improved particle swarm optimization (IPSO) methodology is employed for hyperparameter optimization of the gated recurrent unit and convolutional neural network (GRU‐CNN) model, demonstrating superior performance to conventional optimization methods such as PSO and random search. The IPSO‐GRU‐CNN model is extensively compared with various deep learning architectures, including recurrent neural networks (RNN), CNN, long short‐term memory (LSTM), GRU, and CNN‐GRU, to assess its classification accuracy and efficiency. The suggested AIoT framework optimizes the fault detection process and demonstrates a practical and scalable solution for industrial applications, significantly reducing labor costs and capital expenses associated with the maintenance services of firefighting pumps. Experimental results demonstrated that the developed framework outperforms conventional techniques in terms of classification accuracy and error. Comparing across conventional techniques, IPSO‐GRU‐CNNs acquire the most significant enhancements of 73.37% loss, 98.88% validating loss, 25.84% CP, 89.72% validating CP, 74.64% MAE, 97.36% validating MAE, 74.21% MSE, 99.9% validating MSE, 5.8% PRE, 5.78% validating PRE, 5.06% REC, and 5.2% validating REC. This framework offers a robust and efficient solution for predictive maintenance in firefighting pump systems, facilitating early fault detection and reducing downtime.

Journal

Computational Intelligence – Wiley

Published: Jun 1, 2025

Keywords: artificial intelligent of things; convolutional neural network; firefighting pumps; gated recurrent unit; hyperparameter fine‐tuning; improved particle swarm optimization; maintenance framework

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