Fault Detection from Horizontal Shaft Centrifugal Pump Fan Sound Analysis Using Artificial Intelligence

Abstract

Axial misalignment, over-forced and wear of the components that constitute the machine is changed in the sound. It is of critical importance to implement early fault diagnosis and predictive maintenance planning in order to prevent errors caused by machines that break down or fail during operation. In this study, data comprising 15 one-dimensional sequences and 15 two-dimensional images from MFCCs (Mel-Frequency Cepstral Coefficients) for each sound were utilized in CNN (Convolutional Neural Networks). Furthermore, the data used in ML (Machine Learning) models were created by extracting 28 features from various audio characteristics such as amplitude-time, mel-spectrogram, MFCCs, ZCRs (Zero Crossing Rates), and RMS (Root Mean Square) energy. SVM (Support Vector Machine), KNN (K-Nearest Neighbours) and EL (Ensemble Learning), which combines SVM, KNN and RF (Random Forest) models, were utilized. The results indicated that the accuracy rates varied between 76.21% and 99.59%. The EL model exhibited the highest accuracy, correctly predicting all 99 sounds for faulty, 248 sounds out of 249 sounds for slightly faulty and 143 sounds out of 144 sounds for intact. The results indicate that it is possible to diagnose faults in centrifugal pumps and preventing errors. Consequently, economic savings will be achieved by reducing the losses caused by faulty parts and energy loss caused by the decrease in the efficiency of the system when it operates incorrectly will be prevented.

Keywords

• Artificial Intelligence
• Classification models
• Centrifugal pump fan
• Early fault diagnosis
• Predictive maintenance

Thanks

IIC2024(Uluslararası Bilişim Kongresi-2024) kongresinde bu dergide yayınlanmak üzere bildirimizi seçtiklerinden dolayı teşekkür ederiz.

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