Unsupervised Multi-Sensor Condition Monitoring of AODD Pump Systems Using Physics-Informed Health Indices and Gaussian Mixture Models

Abstract

Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper presents an unsupervised multi-sensor framework that requires no fault labels, integrating physics-informed dual health indices, HI-P (sludge load) and HI-V (mechanical stress), with a Gaussian Mixture Model anomaly detector and a physics residual attribution module. Governing equations motivate the use of these indices from five sensors: inlet and outlet flow meters (100 Hz), an air pressure transducer (100 Hz), and inlet and outlet accelerometers (1652 Hz). Trained on one healthy baseline day (86,218 one-second windows), the Gaussian Mixture Model achieves 100% day-level classification performance on the evaluated dataset (F1 = 1.00) across 455,201 test windows from nine operating days, with window-level receiver operating characteristic area under the curve (ROC-AUC) = 0.8580 and precision–recall AUC (PR-AUC) = 0.9082. Residual attribution analytically confirms that pressure residuals drive Episode 1 (HI-P peak 3.63 times baseline, Cohen’s d = 1.70) and vibration residuals drive Episode 2 (HI-V peak 5.44 times the baseline, d = 4.10), providing empirical support for the proposed physics-informed formulation without requiring fault labels. Comparisons with four unsupervised benchmarks confirm that this is the only approach that simultaneously enables label-free operation, physics-driven features, exact attribution, real-world deployment, and perfect day-level F1.