Diagnosis of Submersible Centrifugal Pumps: A Motor Current and Power Signature Approaches

Hernández-Solís, Augusto; Carlsson, Fredrik

doi:10.1080/09398368.2010.11463749

Cited by 20 publications

(18 citation statements)

References 3 publications

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“…In recent years the ultrasonic detection applied to measure the oil film thickness of rolling bearing lubrication [111][112][113], in order to obtain the state of rolling bearing [114]. Current signature Motor detection in recent years has begun to apply to fault diagnosis of rolling bearings, which is mainly used in the deep buried of the centrifugal pump [115][116], nuclear reactor [117] and asynchronous motor drive [118][119], etc..…”

Section: Other Analytical Techniquesmentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

164

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A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings' performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents, equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, finally the advantages, disadvantages and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.

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Section: Other Analytical Techniquesmentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

164

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“…e contribution rate of each eigenvalue P λ was calculated according to formula (7). e threshold θ is usually equal or greater than 0.9 by experience.…”

Section: Feature Reduction Based On Pcamentioning

confidence: 99%

“…Durocher and Feldmeier [6] used stator currents to detect cavitation status in a centrifugal pump. Hernandez-Solis and Carlsson [7] measured motor current and voltage signals for different operating points of the pump and studied the correlation between the cavitation phenomena and the power of the motor.…”

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

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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.

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“…In (Harris et al, 2004), the authors describe a fault detection system for diagnosing potential pump system failures using fault features extracted from the motor current and the predetermined pump design parameters. In (Hernandez-Solis & Carlsson, 2010), the motor current and power signatures are analyzed to not only detect when cavitation in the pump is present, but also when it starts. The correlation between the pump cavitation phenomena and the motor power is studied at different pump operating conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis of Centrifugal Pumps Using Motor Electrical Signals

Harihara¹,

Parlos²

2012

Centrifugal Pumps

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Diagnosis of Submersible Centrifugal Pumps: A Motor Current and Power Signature Approaches

Cited by 20 publications

References 3 publications

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

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

Fault Diagnosis of Centrifugal Pumps Using Motor Electrical Signals

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