Application of a modified MOPSO algorithm and multi-layer artificial neural network in centrifugal pump optimization

Centrifugal pumps are commonly utilized in thermo-fluidic systems in the industry. Being a rotating machinery, they are prone to vibrations and their premature failure may affect the system predictability and reliability. To avoid their premature breakdown during operation, it is necessary to diagnose the faults in a pump at their initial stage. This study presents the methodology to diagnose fault of a cent rifugal pump using two distinct machine learning techniques, namely, Support vector machine (SVM) and Artificial neural network (ANN). Different statistical features are extracted in the time and the frequency domain of the vibration signal for different working conditions of the pump. Furthermore, to decrease the dimensionality of the obtained features different feature ranking (FR) methods, namely, Chi-square, ReliefF and XGBoost are employed. ANN technique is found to be more efficient in classifying faults in a centrifugal pump as compared to the SVM, and Chi-square and XGBoost ranking techniques are better than ReliefF at sorting more relevant features. The results presented in thus study demonstrate that an ANN based machine learning approach with Chi-square and XGBoost feature ranking techniques can be used effectively for the fault diagnosis of a centrifugal pump.

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Performance Prediction for a Centrifugal Pump with Splitter Blades Based on BP Artificial Neural Network

Communications in Computer and Information Science - Life System Modeling and Intelligent Computing ◽

10.1007/978-3-642-15859-9_31 ◽

2010 ◽

pp. 223-229

Author(s):

Jinfeng Zhang ◽

Shouqi Yuan ◽

Yanning Shen ◽

Weijie Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Centrifugal Pump ◽

Performance Prediction ◽

Bp Artificial Neural Network ◽

Artificial Neural

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Gas–Liquid Two-Phase Flow Pattern Identification of a Centrifugal Pump Based on SMOTE and Artificial Neural Network

Micromachines ◽

10.3390/mi13010002 ◽

2021 ◽

Vol 13 (1) ◽

pp. 2

Author(s):

Denghui He ◽

Ruilin Li ◽

Zhenduo Zhang ◽

Shuaihui Sun ◽

Pengcheng Guo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flow Pattern ◽

Centrifugal Pump ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Bubble Flow ◽

Phase Flow ◽

Two Phase ◽

Artificial Neural

The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas–liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.

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