Research on State Recognition and Failure Prediction of Axial Piston Pump Based on Performance Degradation Data

Abstract

Degradation state recognition and failure prediction are the key steps of prognostic and health management (PHM), which directly affect the reliability of the equipment and the selection of preventive maintenance strategy. Given the problem that the distinction between feature vectors is not obvious and the accuracy of fault prediction is low, a method based on multi-class Gaussian process classification and Gaussian process regression (GPR) is studied by the vibration signal and flow signal in six degraded states of the axial piston pump. For degradation state recognition, the variational mode decomposition (VMD) was used to decompose the vibration signal, and obtaining intrinsic mode function (IMF) components with rich information. Subsequently, multi-scale permutation entropy (MPE) was employed to select feature vectors of IMF components in different states. In order to reduce feature dimensions and improve recognition performance, ReliefF was used to select feature vectors with high weight, then a method based on multi-class Gaussian process classification was established by using these feature vectors to realize the research on the degradation state recognition. The test results demonstrate that the method can effectively identify the degradation state. Its recognition rate reaches 98.9%. Besides, for failure prediction, through the analysis of the wear process and wear mechanism of the valve plate, the curve fitting between the flow and the wear amount was performed by GPR to realize the failure prediction of the axial piston pump. Depending on the evaluation index, the GPR obtained a better failure prediction effect. The results will assist in the realization of predictive maintenance, and which also has significant practical value in project items.

Research on Identification Method of Wear Degradation of External Gear Pump Based on Flow Field Analysis

“…On the basis of not changing the failure mechanism, combined with the characteristics of the step-accelerated stress test, three stress levels are selected between the rated pressure of 20 MPa and 30 MPa, they are 23 Mpa, 25 MPa and 27 Mpa respectively. According to the industry standard Hydraulic gear pump JBT7014.2-2018, under the rated working condition, the volume efficiency is less than 82%, which is considered as failure [26,27]. Using a quantitative truncation method, when one of the four pumps reaches the specified amount of degradation for two consecutive measurements, the stress is raised to the next stress stage.…”

Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things

“…Maintenance records owned by the engineer will also be recorded. Hence, it uses the abnormal state reported by the employees to analyze the abnormal state of the machine, and it records the abnormal signal of the machine and the engineer's record, such as state recognition and failure prediction for the axial piston pump [21].…”

Hydraulic pump fault diagnosis based on chaotic characteristics of speed signals under non-stationary conditions

“…In the past few decades, data-driven fault diagnosis methods have developed rapidly. Various kinds of signals, such as vibration, 2,3 pressure, 4 flow, 5 and current 6 are measured to dig deeper information about the reliability of pumps. However, flow and pressure monitoring methods commonly need to intervene in the operation for measurement.…”