Data-Driven Predictive Maintenance for Circulating Water Pumps: A Statistical Approach

Predictive maintenance (PdM) has proven to be a critical strategy for minimizing downtime and optimizing operational efficiency in industrial systems. In this work the authors propose a data-driven PdM framework for circulating water pumps (CWPs), combining statistical analysis with machine learning. This begins with the analysis of a multivariate time-series dataset consisting of 51 sensors monitoring motor electrical parameters, pump hydraulics, vibration, temperatures, broken states, recovery states and finally normal states. With the help of a statistical analysis, utilizing boxplots and both Pearson and Spearman correlation indices, the aim is to identify the key degradation parameters. Notably, motor phase current (Pearson r = −0.872), pump vibration (r = −0.809), and discharge pressure (r = −0.732) showed the strongest negative correlation with failure states, revealing a complete system shutdown pattern during failure conditions. Applying these results to the development of a Random Forest classification model was the next step. Due to class imbalance and label interpretation challenges, i.e., normal state, broken state and recovery state, the reformulation of the multi-class problem into a binary task focusing on “at-risk” states was executed. Therefore, this led to the critical finding of the reversal of conventional state labels, where RECOVERING states were found to correspond to inactive post-shutdown conditions requiring maintenance intervention, whereas BROKEN states exhibited characteristics more consistent with partial degradation. Lastly, the statistically selected feature model achieved a Recall of 0.76, a Precision of 0.70 and an F1-score of 0.73. Furthermore, a physics-based grouped subsystem representation combining vibration, electrical, hydraulic, thermal and rotational measurements substantially improved classification performance, achieving a Precision = 0.997, a Recall = 0.992 and an F1-score = 0.995. These results demonstrate the effectiveness of combining statistical analysis, engineering knowledge and machine learning for the predictive maintenance of industrial pumping systems.