Probability-driven identification mechanism for degradation of magnetic drive pumps

Abstract

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.

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

“…In recent years, data-driven models based on historical data features have become a popular alternative method in centrifugal pump research [10][11][12][13][14]. These models can be designed even if the design team lacks a complete understanding of the internal flow field of the centrifugal pump, relying solely on a large amount of data and mature development experience [15][16][17][18].…”