Reliability analysis method of coupling optimal importance sampling density and multi-fidelity Kriging model

Xie, Jiayu; Tian, Zongrui; Zhi, Pengfei; Zhao, Yuyuan

doi:10.17531/ein/161893

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…In order to further verify the effectiveness of the proposed method in dealing with higher dimensional problems, the classical nonlinear system with six random variables is selected from the literature. 6,10,30,31 The system structure is shown in Figure 9 and the performance function is denoted as…”

Section: Example 3: a Nonlinear Oscillatormentioning

confidence: 99%

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

Sun

Liu

Zhi

et al. 2023

Quality & Reliability Eng

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Due to the limited sample sizes and highly complicated performance functions, how to improve the reliability calculation accuracy and efficiency is an important issue for complex equipment working in harsh environments. This paper proposes an active learning reliability algorithm based on the double‐mutation slap swarm algorithm‐optimized (DMSSA) Kriging surrogate model, parallel infilling strategy and subset simulation (SS). In the method, the uniform design (UD) is employed to select the initial sampling points and their real responses of performance functions are estimated. The DMSSA integrates Cauchy and Gauss mutation to explore the optimal hyperparameter of Kriging model with high accuracy. The parallel infilling strategy combines the expected improvement (EI), minimizing prediction (MP) and mean squared error (MSE) criteria to find new sampling points, which are adaptively added into the database in each iteration to update the design of experiment (DoE). Ultimately, the SS method is employed to deal with the small failure probability problems. Five various examples are analyzed to verify the calculation accuracy and efficiency of the proposed method.

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Section: Example 3: a Nonlinear Oscillatormentioning

confidence: 99%

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

Sun

Liu

Zhi

et al. 2023

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bearing preloads can be applied in various ways, such as electromagnetic control, hydraulic control, and centrifugal force control [6][7][8][9]. The mechanical control method offers the benefits of automation and stability, enabling more precise torque and position control while providing a consistent control torque.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Jiang,

Han,

Chang

2024

CMES

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Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a neural network is used to optimize the fitting of the preload data. The limitations of conventional preload testing methods are identified, and the integration of complementary information from multiple sensors is used to achieve accurate predictions, offering valuable insights into the optimal preload force. Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

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“…In reference [19], a subregion computing strategy is proposed, and a new learning function is introduced, which can effectively improve the computing efficiency of time-varying reliability. In reference [20], a new coupled Kriging model is proposed to balance computational costs and accurate results.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of a Three-Engine Simultaneous Pouring Control System Based on Bayesian Networks Combined with FMEA and FTA

Cui,

Zheng,

Wang

et al. 2023

Applied Sciences

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Pouring is an important process in the production of solid propellant rocket engines, and usually, the cost of a solid propellant rocket engine is extremely high. Therefore, pouring production with high reliability is very important. The pouring of three engines of solid propellant rocket engines simultaneously can greatly improve its production efficiency. However, it makes the system more complex and redundant. For a multi-state system, it is difficult to make an accurate evaluation of system reliability. Aiming at the redundancy of multiple engines and acousto-optic combined control in the three-engine simultaneously slurry pouring alarm control system with dissimilar redundant alarm units, a reliability analysis method is proposed based on the combination of Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The control system is divided into several redundant states according to the alarm function, and then the Bayesian Networks method is used for reliability evaluation and calculation. Finally, the reliabilities of systems with dissimilar redundancy degrees are obtained. The tangible results of this research work are as follows: (1) The research results obtained by applying the FMEA method laid a foundation for the establishment of a fault trees model for analyzing the reliability of the control system using the FTA method, in addition, which can guide the maintenance and fault identification of the control system during engineering application. (2) The calculated value of the reliability of the control system is 0.999989, and the mean time between failures is MTBF is 5 × 104 by using the fault tree analysis method, which proves that the designed three-engine simultaneous pouring system is very reliable. (3) Based on the calculation and comparison of the Bayesian Networks of redundant three-engine pouring control systems, the circuit diagram of the improved control system is identified.

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Reliability analysis method of coupling optimal importance sampling density and multi-fidelity Kriging model

Cited by 4 publications

References 28 publications

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Reliability Analysis of a Three-Engine Simultaneous Pouring Control System Based on Bayesian Networks Combined with FMEA and FTA

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