Acoustic Based Cavitation Detection of Centrifugal Pump by Neural Network

Farokhzad, Saeid; Ahmadi, Hojjat

doi:10.18005/jmet0101001

Cited by 10 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Farokhzad et al employed a multilayer perceptron neural network to classify features extracted from acoustic signals. The results demonstrated 98% precision in detecting cavitation [14]. The main challenge to detect faults with acoustic emission is the high cost of the related sensors.…”

Section: Introductionmentioning

confidence: 92%

“…The results demonstrated 98% precision in detecting cavitation [14]. The main challenge to detect faults with acoustic emission is the high cost of the related sensors.…”

Section: Introductionmentioning

confidence: 92%

“…(13) and (14). Therefore, the residual can be obtained using electrical sensors and a pressure sensor.…”

Section: Fault Detection Algorithmmentioning

confidence: 99%

“…Consequently, the unknown variables such as angular speed and fluid flow in the residual equation are obtained using Eqs. ( 13) and (14). Therefore, the residual can be obtained using electrical sensors and a pressure sensor.…”

Section: Fault Detection Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Cavitation detection in centrifugal pumps using pressure time-domain features

Samanipour¹,

Poshtan²,

Sadeghi³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Condition monitoring of centrifugal pumps is vital due to their crucial role in industries. One of the most prevalent faults in pumps is cavitation, which can cause mechanical faults or even failure in the pump. In this paper, an approach is suggested to detect cavitation in a centrifugal pump using time-domain analysis of the pressure signal residual. First, pressure and torque signals are obtained using a model of the electro-pump, and then pressure deviation from the pump performance curve is defined as a residual. The residual time-domain features are extracted and applied as inputs to a self-organizing map (SOM) neural network to classify the system modes. The results indicate that the suggested method is capable of detecting incipient cavitation. Furthermore, the suggested method demonstrates robust performance against disturbance.

show abstract

Section: Introductionmentioning

confidence: 92%

“…The results demonstrated 98% precision in detecting cavitation [14]. The main challenge to detect faults with acoustic emission is the high cost of the related sensors.…”

Section: Introductionmentioning

confidence: 92%

“…(13) and (14). Therefore, the residual can be obtained using electrical sensors and a pressure sensor.…”

Section: Fault Detection Algorithmmentioning

confidence: 99%

Section: Fault Detection Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Cavitation detection in centrifugal pumps using pressure time-domain features

Samanipour¹,

Poshtan²,

Sadeghi³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Alfayez et al [4] presented a case study where acoustic emission has been applied for detecting incipient cavitation and determining the best efficiency point (BEP) of a 60 kW centrifugal pump. Farokhzad and Ahmadi [5] concentrated on a procedure for prediction of cavitation using acoustic signals and multilayer perceptron neural network. Durocher and Feldmeier [6] used stator currents to detect cavitation status in a centrifugal pump.…”

Section: Introductionmentioning

confidence: 99%

Cavitation Detection in Centrifugal Pump Based on Interior Flow‐Borne Noise Using WPD‐PCA‐RBF

Zhu

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow-borne noise are measured under three different flow conditions. Then, time-frequency domain analysis is performed on the interior flow-borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six-feature parameters are extracted based on frequency-division statistics, including three time-domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow-borne noise analysis can be well applied for on-line monitoring and diagnosis of pump industry.

show abstract