A new model for reliability design and reliability sensitivity analysis of a hydraulic piston pump

Zhang, Tianxiao; Zhang, Yimin

doi:10.1177/1748006x16681713

Cited by 10 publications

(10 citation statements)

References 26 publications

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“…The trajectory of the slipper on the swash plate is an ellipse. 24 When the piston is located at point A , the position coordinate of the slipper at point A ’ in the o ’ x ’ y ’ z ’ can be given by…”

Section: Torque and Flow Analysis Of A Single Piston-slipper Groupmentioning

confidence: 99%

A time-varying reliability-based robust design method considering overall efficiency of axial piston pump

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Axial piston pumps (APPs) are the core energy conversion components in a hydraulic transmission system. Energy conversion efficiency is critically important for the performance and energy-saving of the pumps. In this paper, a time-varying reliability design method for the overall efficiency of APPs was established. The theoretical and practical instantaneous torque and flow rate of the whole APP were derived through comprehensive analysis of a single piston-slipper group. Moreover, as a case study, the developed model for the instantaneous overall efficiency was verified with a PPV103-10 pump from HYDAC. The time-variation of reliability for the pump was revealed by a fourth-order moment technique considering the randomness of working conditions and structure parameters, and the proposed reliability method was validated by Monte Carlo simulation. The effects of the mean values and variance sensitivity of random variables on the overall efficiency reliability were analyzed. Furthermore, the optimized time point and design variables were selected. The optimal structure parameters were obtained to meet the reliability requirement and the sensitivity of design variables was significantly reduced through the reliability-based robust design. The proposed method provides a theoretical basis for designers to improve the overall efficiency of APPs in the design stage.

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Section: Torque and Flow Analysis Of A Single Piston-slipper Groupmentioning

confidence: 99%

A time-varying reliability-based robust design method considering overall efficiency of axial piston pump

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Sensitivity analysis in the reliability analysis can identify the contributions of environment variables on the reliability level. 23,24 The reliability sensitivity is defined as the partial derivative of the reliability function with respect to the variable under the given stress level. Taking independent failures as an example, the sensibility function can be written as follows…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Reliability sensitivity functions of the average power x 1 and the numbers of tool changing x 2 are obtained by equations (23) and (24), which are drawn in Figures 4 and 5, separately. The estimated coefficients are shown in Table 5.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Reliability assessment of the spindle systems with a competing risk model

Yang

Chen

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Traditional reliability assessment of spindle systems of machine tools suffers from long testing time and high cost. Accelerated life testing is an alternative that overcomes the shortcomings of traditional reliability testing. In a life testing, identification of critical factors of service life and an accurate model are important. Based on the characteristic analysis and engineering experience, four reliability factors, which are the average power of spindle systems, the number of tool changing, the number of spindles restarting and environment temperature, are selected as accelerating environment variables. An accelerated failure time model is used to describe the inverse relationship between the variables and reliability for the catastrophic failure mode and the degradation failure mode separately. Then a competing risk model is built by considering competing risks of two modes. Parametric reliability models are proposed to capture the statistical independency and dependency separately, in which the Gumbel–Hougaard copula function is used to establish the joint cumulative distribution for dependency. Thereby the hypothesis testing is developed to determine the failure modes dependency. The reliability sensitivity of each environment variable is analyzed. Finally, the proposed model is illustrated with a real field case study.

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“…In the RBDO, the gradient of the reliability with respect to the design variables is also called the reliability sensitivity. Theories and methods of reliability sensitivity analysis are roughly divided into two categories: approximation-based methods and simulation-based methods (Valdebenito et al 2018;Yanfang et al 2011;Zhang and Zhang 2016).…”

Section: Introductionmentioning

confidence: 99%

A gradient-based uncertainty optimization framework utilizing dimensional adaptive polynomial chaos expansion

Fang

Gong

et al. 2018

Struct Multidisc Optim

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To improve the efficiency of solving uncertainty design optimization problems, a gradient-based optimization framework is herein proposed, which combines the dimension adaptive polynomial chaos expansion (PCE) and sensitivity analysis. The dimensional adaptive PCE is used to quantify the quantities of interest (e.g. reliability, robustness metrics) and the sensitivity. The dimensional adaptive property is inherited from the dimension adaptive sparse grid, which is used to evaluate the PCE coefficients. Robustness metrics, referred to as statistical moments, and their gradients with respect to design variables are easily derived from the PCE, whereas the evaluation of the reliability and its gradient require integrations. To quantify the reliability, the framework uses the Heaviside step function to eliminate the failure domain and calculates the integration by Monte Carlo Simulation with the function replaced by PCE. The PCE is further combined with Taylor's expansion and the finite difference to compute the reliability sensitivity. Since the design vector may affect the sample set determined by dimension adaptive sparse grid, the update of the sample set is controlled by the norm variations of the design vector. The optimization framework is formed by combining reliability, robustness quantification and sensitivity analysis and the optimization module. The accuracy and efficiency of the reliability quantification, as well as the reliability sensitivity, are verified through a mathematical example, a system of springs, and a cantilever beam. The effectiveness of the framework in solving optimization problems is validated by multiple limit states example, a truss optimization example, an airfoil optimization example and an ONERA M6 wing optimization problem. The results demonstrate that the framework can obtain accurate solutions at the expense of a manageable computational cost.

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A new model for reliability design and reliability sensitivity analysis of a hydraulic piston pump

Cited by 10 publications

References 26 publications

A time-varying reliability-based robust design method considering overall efficiency of axial piston pump

A time-varying reliability-based robust design method considering overall efficiency of axial piston pump

Reliability assessment of the spindle systems with a competing risk model

A gradient-based uncertainty optimization framework utilizing dimensional adaptive polynomial chaos expansion

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