# Short-Horizon Predictive Maintenance of Industrial Pumps Using Time-Series Features and Machine Learning

## TL;DR

This paper develops a machine learning framework using time-series features to predict short-term faults in industrial pumps, demonstrating the importance of lookback window length for accurate early warnings.

## Contribution

It introduces a scalable, interpretable predictive maintenance method that optimally uses historical sensor data for fault forecasting in industrial pumps.

## Key findings

- Random Forest achieved up to 69.2% recall at 5 minutes with a 60-minute window.
- Optimal lookback window varies with prediction horizon and fault pattern.
- The approach effectively integrates real-time sensor data for proactive maintenance.

## Abstract

This study presents a machine learning framework for forecasting short-term faults in industrial centrifugal pumps using real-time sensor data. The approach aims to predict {EarlyWarning} conditions 5, 15, and 30 minutes in advance based on patterns extracted from historical operation. Two lookback periods, 60 minutes and 120 minutes, were evaluated using a sliding window approach. For each window, statistical features including mean, standard deviation, minimum, maximum, and linear trend were extracted, and class imbalance was addressed using the SMOTE algorithm. Random Forest and XGBoost classifiers were trained and tested on the labeled dataset. Results show that the Random Forest model achieved the best short-term forecasting performance with a 60-minute window, reaching recall scores of 69.2\% at 5 minutes, 64.9\% at 15 minutes, and 48.6\% at 30 minutes. With a 120-minute window, the Random Forest model achieved 57.6\% recall at 5 minutes, and improved predictive accuracy of 65.6\% at both 15 and 30 minutes. XGBoost displayed similar but slightly lower performance. These findings highlight that optimal history length depends on the prediction horizon, and that different fault patterns may evolve at different timescales. The proposed method offers an interpretable and scalable solution for integrating predictive maintenance into real-time industrial monitoring systems.