Abstract
To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. In this method, the Mahalanobis distance is used to replace the distance function in the conventional KNN algorithm. Grid search and cross-validation are used to determine the optimal K value of the prediction model. A centrifugal pump test rig was established to solve three common faults of centrifugal pumps: cavitation, impeller damage, and machine seal damage, and the method was verified. The results show that this method can effectively distinguish the specific fault types of centrifugal pumps based on vibration signals, and the fault prediction accuracy of the off-balance condition is up to 82%. This study provides a novel idea and method for centrifugal pump fault prediction and diagnosis and avoids the interaction between parameters when monitoring multiple parameters.
Highlights
Centrifugal pump is an important fluid conveying equipment; it is widely used in various fields and plays a significant role in the development of the national economy. erefore, it is necessary to ensure the normal and stable operation of the centrifugal pump.In the process of centrifugal pump operation, the earliest indication of failure is usually the abnormal vibration signal
Approaches to extract features from mechanical systems based on timedomain data were proposed in [2, 3]. e vibration signal of the centrifugal pump can be analyzed and the fault of the centrifugal pump can be performed
Xue et al [4] proposed a fault prediction system. e vibration signal of a centrifugal pump is analyzed in the amplitude domain and time domain, and the characteristic structure of the signal in the frequency domain is analyzed by the fast Fourier transform (FFT) signal analysis method. e failure of the centrifugal pump was analyzed based on the database of typical vibration failure cases
Summary
Centrifugal pump is an important fluid conveying equipment; it is widely used in various fields and plays a significant role in the development of the national economy. erefore, it is necessary to ensure the normal and stable operation of the centrifugal pump. Erefore, vibration signal monitoring has become the most commonly used method in centrifugal pump fault diagnosis. Is paper proposes a centrifugal pump fault monitoring system based on an improved KNN algorithm [22] based on the Mahalanobis distance. The ReliefF algorithm [23] was utilized to carry out weight analysis on the timedomain and frequency-domain features [24] and parameters commonly used in centrifugal pump monitoring. 3. Feature Engineering Based on ReliefF Algorithm ere are several parameter indexes to evaluate the operation condition of the centrifugal pump; if all parameters are selected at the same time, it will be difficult to judge the operating condition of the centrifugal pump because of the mutual influence between the parameters, and the operation fault cannot be accurately predicted.
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