Research on Pump Condition Prediction Based on Ensemble Learning Strategies

Failures in water pumps can lead to shutdowns, production disruptions, and resource wastage, particularly in areas such as industrial production, water supply, and flood control, where the impact is significant and the losses immeasurable. Therefore, monitoring and early warning to enhance the operational efficiency and stability of water pumps is crucial. Based on research into deep learning algorithms, this paper proposes and implements a pump condition prediction model using an ensemble learning strategy. By constructing three different models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN), the states of the water pump within different time windows are predicted respectively. Among them, the LSTM model performs better, achieving an accuracy rate of 98.3%. Finally, an ensemble learning strategy based on voting is employed to integrate the prediction results from the LSTM, CNN, and DNN models, achieving an accuracy rate of 98.5%. This approach improves the overall prediction performance.