Fig 9 - uploaded by Maamar Ali Altobi
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Cavitation vibration signal at a speed of 40 Hz (a) time domain (b) random high frequencies represent cavitation at frequency domain
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Centrifugal pumps are rotating machines which are widely used in process operations and other applications. Efficient and failure-free operation of these pumps is important for effective plant operation and productivity. However , the complexity of pumps, combined with continuous operation, can lead to failure and expensive maintenance requirements...
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Citations
... Sedangkan analisis berbasis spektrum kurang efektif dalam mendeteksi kavitasi karena fast fourier transform (FFT) mengasumsikan bahwa konten frekuensi sebuah sinyal adalah konstan. Sedangkan kavitasi menghasilkan konten frekuensi yang bervariasi terhadap waktu [6]. Akibatnya spektrum yang dihasilkan sulit dibaca dan dianalisis. ...
Cavitation is a phenomenon that often occurs in the centrifugal pumps. The impact of cavitation is a decrease in pump performance which will affect the ongoing production process in the industries. It is important to have a method to detect the phenomenon of cavitation early. The vibration signal is a parameter that is often used in detecting cavitation or other faulty components. One of the methods is based on the pattern recognition i.e. machine learning. Linear Discriminant Analysis (LDA) is a machine learning algorithm that has the advantage of reducing the parameters used into low dimensions without reducing the accuracy of their classification. The study proposes LDA to classify normal conditions, initial cavitation, intermediate cavitation and severe cavitation. The recording of the vibration signal is taken using the an accelerometer mounted on the inlet of the centrifugal pump. The vibration signal is then extracted using 10 statistic parameters of time domain as the LDA feature selection, namely mean, RMS, standard deviation, kurtosis, skewness, crest factor, clearance factor, shape factor, variance and peak value. The results shows that the LDA classifier can detect and classify cavitation conditions with an accuracy rate of 98.8% on training and 99.6% on testing. The shape factor, kurtosis, skewness and RMS parameters are a combination of parameters that have a large contribution to the classifier to detect and classify cavitation conditions.Keywords: Linear Discriminant Analysis (LDA), cavitation, centrifugal pump, statistical parameter
Machine condition monitoring is a primordial field of study. It allows to avoid downtime in industrial plants, avoiding financial and time losses. In this article, we use an IoT framework to classify the pump’s vibration signal, in order to identify a normal stage of operation, an incipient cavitation stage and a severe cavitation stage. Our approach uses the vibration signal, which is collected with a MEMS sensor, as an image. The feature extractors used in this study: Hu’s Moments, Gray Level Co-occurrence Matrix, Local Binary Patterns, DenseNet169, ResNet50, VGG19 and MobileNet. The classifiers used in this paper were: Gaussian Naive Bayes, Support Vector Machines, Random Forest, Multilayer Perceptron and k-Nearest Neighbors (kNN). The results showed that Hu’s Moments combined with kNN achieved the best accuracy (99.47%) with a score time of 17 ms. Thus, our approach is reliable and efficient to detect cavitation in pumps.
