PumpSpectra: An MCSA-Based Platform for Fault Detection in Centrifugal Pump Systems

Reliable detection of faults in centrifugal pump systems is challenging in industrial environments due to harsh operating conditions, limited sensor access, and the need for fast, explainable decisions. We developed PumpSpectra, an industrial Motor Current Signature Analysis (MCSA) platform that processes uploaded stator-current CSV files using FFT/STFT with transparent, rule-based models designed to identify mechanical faults including misalignment, bearing defects, and impeller anomalies; field validation demonstrated misalignment detection. In a case study at the El Oued desalination plant (Algeria; n=40 operating points), PumpSpectra achieved 91.2% diagnostic accuracy with a 95% reduction in analysis time compared to manual MCSA post-processing, and a false-positive rate of 3.8% at 0.1 Hz resolution. These results suggest that current-only, explainable analytics can support predictive maintenance programs by accelerating fault triage, improving traceability of decisions, and reducing avoided maintenance costs in pump-driven industrial assets.

• centrifugal pump
• motor current signature analysis
• fault detection
• stator current
• misalignment detection
• bearing defects
• predictive maintenance
• rule-based models
• explainable analytics
• desalination plants

• 9 — Industrie, innovation et infrastructure

• Machine Fault Diagnosis Techniques
• Fault Detection and Control Systems
• Oil and Gas Production Techniques
• centrifugal pump
• motor current signature analysis
• fault detection
• stator current
• misalignment detection
• bearing defects
• predictive maintenance
• rule-based models
• Control and Systems Engineering
• Ocean Engineering
• Engineering