Residual life prediction of compressor impeller with microcrack damage

Qu, Anbang; Li, Fanchun

doi:10.1080/15397734.2023.2201338

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The contour integral method and Paris law are used to predict crack propagation. Qu A et al [ 13 ] conducted a comprehensive analysis of the mechanical and physical quantities of the product and studied the crack propagation law and residual life of the product. The model-driven prediction method establishes a physical or mathematical model to describe the degradation process of the object and combines the condition monitoring data to update the model parameters.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

Jiang,

Sheng,

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.

show abstract

Section: Introductionmentioning

confidence: 99%