Deteksi Kavitasi Berbasis Getaran Pada Pompa Sentrifugal Menggunakan Principal Component Analysis (PCA)
DOI:
https://doi.org/10.18196/st.212219Keywords:
statistical parameters, cavitation, centrifugal pumps, Principal Component Analysis, vibration signals.Abstract
A centrifugal pump is one type of pumps that widely used in industries. Its mechanism which creates pressure changes may cause cavitation. Cavitation phenomenon that is not properly maintained may results fatal breakdown leading to high economic losses. Therefore, research is needed to find and develop a method that can detect early cavitation phenomena and identify it at several levels as well. This paper presents a method that can detect cavitation by monitoring the vibrations level of the pump based on statistical analysis of time domain and Principal Component Analysis (PCA). Vibration data is collected, trained and tested for each cavitation level. Training data is normalized and trained for each cavitation level using PCA which produces data loading matrix. The loading matrix is then multiplied by the testing data which gives a score matrix used to classify cavitation level of the centrifugal pump. The result shows that the method of domain-based PCA is successful in transforming the original data of 7 statistical parameters to 7 principal components (PC) with maximum variant. Three PCs gives 93.68% variants which can clearly identify and classify the differences between normal, early, intermediate and fully developed cavitation in the centrifugal pumps.
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