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.

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Publication Details

Journal
Sensors
Published
2025-11-12
DOI
https://doi.org/10.3390/s25226916
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

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

Zoheir Tir, Mohamed Sahraoui, Khaled Laadjal, Hamza Adaika
Sensors
Machine Fault Diagnosis Techniques
article

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

Zoheir Tir, Mohamed Sahraoui, Khaled Laadjal, Hamza Adaika
article en

Abstract

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.

SensorsVol. 25(22)
University of Biskra (DZ), Institut de Recerca en Energia de Catalunya (ES)
Openalex Percentile: Top 57%
Machine Fault Diagnosis Techniques
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