Abstract
Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.
Highlights
Vibration analysis combined with machine learning has been one of the effective techniques for analyzing cavitation in centrifugal pumps as well as other rotating machinery
Except for the fully connected layers, all the layers are transferred to be used in this target task, which is the classification of the bispectrum images. e complete structure of the convolutional neural networks (CNNs) used in this research based on AlexNet is shown in Table 1. e fully connected layers of AlexNet are substituted by two new fully connected layers. e first one has 5 neurons and the second one has 2 neurons. ese layers are trained by the obtained bispectrum from vibration datasets recorded in the experiments. e rest of the layers are identically transferred from AlexNet
The vibrations of a centrifugal pump under different cavitation conditions were recorded and processed using bispectral analysis. e obtained bispectrum was generated from the vibration signals and saved as RGB
Summary
Vibration analysis combined with machine learning has been one of the effective techniques for analyzing cavitation in centrifugal pumps as well as other rotating machinery. The initial machine learning tools opened a door to automatic fault detection of machinery without relying on experts’ knowledge At first, they seemed to be limited and not able to handle a large amount of data or complicated problems; the deep neural networks compensated for the shortcomings of those initial systems. Guo et al [37] used a convolutional neural network and continuous wavelet transform of vibration signals as its input data in order to diagnose rotating machinery faults. Because of limited studies on applying transfer learning and bispectrum analysis for detecting cavitation in pumps, this research is focused on that topic. E generated bispectrum diagrams are used as the input to two pretrained deep neural networks for image processing. e onset of cavitation and the severity level were detected using deep transfer learning. e test results show that the final neural network can accurately detect the occurrence of cavitation in the pump despite the limited number of data used for training. e results of cavitation intensity detection were promising despite the difficulty of the task
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