Health assessment of water pumps using high-dimensional monitoring data

Abstract

With the development of IoT monitoring equipment, an increasing number of monitoring indicators are employed to monitor the operational status of water pumps, thereby resulting in the challenge of data redundancy. This paper proposes an algorithm for predicting the health status of pumps that integrates multiple monitoring variables. Initially, the original dataset is classified using the maximum relevance minimum redundancy method. Next, principal component dimensionality reduction is used to reduce the dimensionality of the classified dataset. Finally, a long and short term memory neural network is employed to construct the association model between monitoring data and equipment health. The proposed algorithm takes into account the correlation between variables and the negative impacts of long-term dependence on the prediction results. It is capable of predicting abnormal working conditions, which has been experimentally verified in the Xiasha Pumping Station located in Hangzhou. The algorithm was compared with LR, SVM, and RNN algorithms, and it was found that the proposed algorithm achieved the highest prediction accuracy. HIGHLIGHTS Sensors measuring vibration, magnetic flux, temperature, and sound loudness have been installed on a water pump to collect data on its operational status.; To address the high dimensionality of the monitoring data, the mRMR-principal component analysis method was employed to reduce the impact of irrelevant variables on the results.; Long short-term memory was used to establish associations between the health status and monitoring data.;

Keywords

• health assessment
• high dimensional data
• information redundancy
• machine learning
• water pumps