A Failure Diagnosis Approach for Firefighting Pump Based Deep Learning GRU Methodology

Firefighting pumps are a critical component of the firefighting system, which directly affects the safety operation of the buildings. Current fault-diagnosing methods for firefighting pumps have limitations and shortcomings due to their complex structure. To improve the failure diagnosing performances of artificial intelligence-based approaches, a GRU framework is developed to quickly identify failure conditions of firefighting pumps, which reduces labor expenses and enhances the quality of maintenance service. Firefighting pumps are installed with sensor devices to simulate various health conditions during the data acquisition. A deep learning approach has been developed to identify different failure types of firefighting pumps. The comparison with other state-of-the-art techniques, including recurrent neural network (RNN), demonstrates the effectiveness of the proposed method, which achieves the ultimate improvements of 17.72% loss, 12.36% MAE, 6.41% validating MAE, 29.36% MSE, and 23.92% validating MSE. The proposed framework has been successfully developed and deployed in Taiwan's firefighting pump manufacturing company.