Numerical method to predict vibration characteristics induced by cavitation in centrifugal pumps

Cao, Ruijia; Yuan, Jianping; Deng, Fanjie; Wang, Longyan

doi:10.1088/1361-6501/ac1181

Cited by 14 publications

(4 citation statements)

References 45 publications

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“…Due to poor lubrication, overload, and other reasons, bearing failures will occur. Pitting, peeling, wear, and other features appear on the rolling bearing components, causing bearing wear, vibration, and impact [ 1 , 2 , 23 ]. As the wear continues and intensifies, it is often accompanied by a rise in temperature.…”

Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

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Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.

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Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

Sensors

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“…WPT is a commonly used filtering tool for obtaining a subband signal. Some researchers recommend that this algorithm should be adopted after capturing the raw vibration signal, because pump states can only be mirrored in a specified frequency range [23][24][25].…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

“…In our study, we show that different groups can be broadly classified using the low-frequency sub-band signal. The unknown interference may affect the classifying results, as mentioned by [23], but does not strictly affect the specified sub-band signal. In other words, the sub-band signal in the lower layer may be polluted by broadband noise, but it contains more potential information that can be utilised in the function calculating procedure.…”

Section: Decomposition Effectmentioning

confidence: 99%

Probability-driven identification mechanism for degradation of magnetic drive pumps

Cao¹,

Yuan²,

Peng³

et al. 2022

Meas. Sci. Technol.

Self Cite

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Pump-state prediction and health management have entered the intelligent era. Data analysis, feature extraction, and automatic classification are the critical stages of the state self-recovery regulation of machines. To explore the identification mechanism of degraded states in magnetic drive pumps, the wavelet packet transform is utilised to filter the raw vibration signals. A classification model is subsequently established based on K-means clustering analysis. The highly sensitive characteristic parameters are accessed via a corresponding pre-processing procedure. Herein, clustering points are acquired, and the detected states are classified. Moreover, the probability of operating states over time is ascertained using hidden Markov models. Thus, the healthy machine states are validated via comparison of the calculated results, indicating that the trigger mechanism can recognise the degraded machine states successfully. The proposed probability-driven identification mechanism makes the automatic identification and intelligent decision-making of self-recovery systems possible, and may be used to provide technical details for application to other rotating machinery systems.

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“…Centrifugal pumps are widely used in industries such as petrochemicals, power, and agricultural irrigation and are an important type of fluid machinery equipment [1][2][3]. In practice, however, most centrifugal pumps are not operating at * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of centrifugal pump performance parameters based on a multilayer hybrid model

Chai

Zhang

Xia

et al. 2023

Meas. Sci. Technol.

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The present paper presents a multilayer hybrid model for sensorless measurement of pump operating status, with the objective of enabling safe and stable operations while reducing energy losses. The model takes easily measurable variables such as rotational frequency and valve opening as input features to predict the performance parameters of the centrifugal pump. By integrating Just-in-time learning (JITL) with Gaussian process regression (GPR) and leveraging the unique probability features of GPR, a JGPR is developed to extract valuable feature information. The JGPR sequentially predicts the flow rate, coefficient for dynamic head, and shaft power. The predicted values are extended to other input features, which can accurately capture the characteristics of the centrifugal pump and effectively replace the process of acquiring process parameters through sensors. Finally, the mechanism model is integrated into the multilayer JGPR model to calculate the performance parameters of centrifugal pump. The validation results indicate a strong agreement between predicted and experimental results, with predicted meeting performance parameters all engineering requirements. Compared to a single model, the multilayer hybrid model significantly improves the reliability of predictions, demonstrating the feasibility of using this approach to predict performance parameters. This research provides valuable insights into the measurement of sensorless pump operating states, enabling safe and efficient operation in complex conditions.

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Numerical method to predict vibration characteristics induced by cavitation in centrifugal pumps

Cited by 14 publications

References 45 publications

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Probability-driven identification mechanism for degradation of magnetic drive pumps

Prediction of centrifugal pump performance parameters based on a multilayer hybrid model

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