Fatigue reliability estimation framework for turbine rotor using multi-agent collaborative modeling

Recent advances in reliability analysis of aeroengine rotor system: a review

“…The reliability analysis of aeroengine rotor system involves the coupling analysis of multiple disciplinaries and collaborative analysis of multiple objectives, which is a complex reliability analysis problem. The schematic diagram of rotor system under multiple physical interaction (MPI) is shown in Figure 12 (Li et al, 2021).…”

“…Based on the linkage splitting procedure and collaborative sampling technique, the DCSM method divides the random variables and output responses at all analysis levels thereby dramatically simplifies the computing tasks. Bai et al proposed a distributed collaborative response surface model and applied it into the probabilistic design of multi-objective and multi-disciplinary radial running clearance reliability analysis of highpressure turbine rotor (Bai and Fei, 2013); Fei et al established decomposed coordinative surrogate model by quadratic polynomial function to evaluate the multi-failure probability of turbine blade disk (Fei et al, 2019); Li et al proposed a multi-agent collaborative modeling approach to fatigue reliability estimation for rotor system (Li et al, 2021), the probabilistic distribution results of mean stress σ m , strain range Δ« t and fatigue life N f are acquired as shown in Figures 13-15; Gao et al developed the quadratic polynomial function-based DCSM to enhance computational efficiency and accuracy in multi-component damage evaluation (Gao et al, 2020b); Song et al presented the neural network regression model instead of QP functions for distributed collaborative probabilistic design of multi-failure structure, the analysis results are shown in Figure 16 (Song et al, 2019c).…”

Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

“…The risk function ℎ(𝑡) is the conditional probability of functional equipment failure at instant (𝑡 + 𝛥𝑡), given by [5,43] :…”

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

“…This method effectively deals with the presence of random noise in the measurements and supports a significantly lower computational load compared to existing methods, to improve the reliability and energy efficiency of gas turbines. And Li et al [2] estimated the fatigue reliability of a turbine using collaborative multi-agent modeling, to improve the calculation precision and the efficiency of the simulation of fatigue reliability estimation for the turbine rotor, this modeling approach makes it possible to have high efficiency and high precision for estimating the fatigue reliability of the studied turbine rotor. Also, Kiaee and Tousi [3] determined the deterioration indices based on a modeling of the prognosis of the gas path of gas turbines, this study allows to improve knowledge on the monitoring of the state of the studied turbine with an increase of 3.29%, and the average remaining useful life of this turbine.…”