Abstract
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.
Highlights
In the fields of industrial production and municipal water affairs, centrifugal pumps have a wide range of applications [1,2]
Azadeh et al [7] proposed a flexible algorithm to classify the condition of pumps based on support vector machine hyper-parameters optimization and artificial neural based on support vector machine hyper-parameters optimization and artificial neura works
The wireless sensor is connected with the cloud platform data layer interface through NB-Internet of Things (IoT) [34,35] to form a wireless application scheme; the wired sensor result is connected to the data collector, and the data collector uploads the collected result to the cloud server through 4G communication to form a wired application
Summary
In the fields of industrial production and municipal water affairs, centrifugal pumps have a wide range of applications [1,2]. Orrù et a introduced a simple and easy machine learning model for early fault prediction of a trifugal pump in the oil and gas industry. The method was applied to a simple and easy machine learning model for early fault prediction of a nose incipient cavitation failures in a water centrifugal pump. Learning [10] proposed fault diagnosis for multistage centrifugal pumps usin and classified ensuring good prediction accuracy. Gonçalves et al [9] presented a novel output-only method based extraction of the collected state data, to achieve the purpose of classifying equipmen on the Markov parameters to diagnose faults. Proposed fault diagnosis methodthe for multistage using informative ratio principal analysis.the These studies mainly on ating status information directlycomponent affects whether actual project canfocus be successfull the feature extraction of the collected state data, to achieve the purpose of classifying plemented.
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