Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique

Azizi, Raziyeh; Attaran, Behrooz; Hajnayeb, Ali; Ghanbarzadeh, Afshin; Changizian, Maziar

doi:10.1016/j.measurement.2017.05.020

Cited by 75 publications

(38 citation statements)

References 27 publications

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“…Hybrid models use a filter model to guide a wrapper model to solve these problems of filter and wrapper methods [46,54-56]. In summary, for stroke risk detection, traditional feature selection methods have a variety of limitations, negatively affecting the quality of selected features and the performance of stroke risk detection models.…”

Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

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Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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“…It needs fine integration and coupling between the conservation and cavitation modeling equations. Azizi et al [ 36 ] presented a system for detection of cavitation severity in centrifugal pumps and for the improvement in its accuracy using a hybrid feature selection technique. A generalized regression neural network (GRNN) is used for cavitation identification.…”

Section: Investigation and Identification For Slurry Erosion And Cmentioning

confidence: 99%

Strive to Reduce Slurry Erosion and Cavitation in Pumps through Flow Modifications, Design Optimization and Some Other Techniques: Long Term Impact on Process Industry

et al. 2021

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Centrifugal pumps are being widely used in various industries for moving fluids that carry solids through pipelines where the need of head and flow rate is not high. Slurry erosion and cavitation are an extremely complex and not yet fully understood phenomenon that occur in centrifugal pumps; however, these undesirable phenomena can be reduced to a certain extent. Appropriate design and development of experiments is required to reasonably predict slurry erosion and cavitation. However, CFD methodology complements analytical solutions and experiments whenever testing of equipment has limitations. The current paper highlights the various slurry erosion and cavitation reduction techniques utilized by different researchers. Economic analysis conducted for a case study relevant to centrifugal pump (CP) usage in Pakistan shows that an 8% enhancement in pump efficiency can reduce the life cycle cost to about 17.6%, which could save up to USD 4281 for a single pump annually in Pakistan.

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“…Vibration monitoring is one of the most widely used and most appropriate techniques for rotating machine analysis [5], so much so that it has been used in recent works as in Azizi [3], which used vibrational analysis for cavitation detection in centrifugal pumps. Stopa [62] presented a study of the frequency spectrum in vibration signals in rotary pumps that had failed.…”

Section: Faults On Wind Turbinesmentioning

confidence: 99%

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

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et al. 2019

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How to cite this paper: Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. AbstractThe eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature. Kiviluoma [29] analyzed the behavior of the power variation in different regions and demonstrated that rapid changes in local wind conditions cause power fluctuations in the IoT system and operational costs of a wind farm

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Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique

Cited by 75 publications

References 27 publications

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Strive to Reduce Slurry Erosion and Cavitation in Pumps through Flow Modifications, Design Optimization and Some Other Techniques: Long Term Impact on Process Industry

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

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