Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures

Peng, Weiwen; Shen, Lijuan; Shen, Yan; Sun, Qiaomei

doi:10.1016/j.ress.2017.11.016

Cited by 42 publications

(32 citation statements)

References 42 publications

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“…To improve the performance of blades, it is necessary to analyze the coupling mechanism between shockwave and boundary layer . Many valuable studies have been proposed to perform this analysis by means of numerical simulations combining with physics of failure modeling . Among them, Wang et al utilized a reliability index strategy and proposed an uncertainty‐based optimization model for the structure design problem with bounded uncertain information.…”

Section: Introductionmentioning

confidence: 99%

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Meng

Yang

Zhang³

et al. 2018

Fatigue Fract Eng Mat Struct

130

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In power and energy systems, both the aerodynamic performance and the structure reliability of turbine equipment are affected by utilized blades. In general, the design process of blade is high dimensional and nonlinear. Different coupled disciplines are also involved during this process. Moreover, unavoidable uncertainties are transported and accumulated between these coupled disciplines, which may cause turbine equipment to be unsafe. In this study, a saddlepoint approximation reliability analysis method is introduced and combined with collaborative optimization method to address the above challenge. During the above reliability analysis and design optimization process, surrogate models are utilized to alleviate the computational burden for uncertainties‐based multidisciplinary design and optimization problems. Smooth response surfaces of the performance of turbine blades are constructed instead of expensively time‐consuming simulations. A turbine blade design problem is solved here to validate the effectiveness and show the utilization of the given approach.

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Section: Introductionmentioning

confidence: 99%

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Meng

Yang

Zhang³

et al. 2018

Fatigue Fract Eng Mat Struct

130

View full text Add to dashboard Cite

show abstract

“…Seyed further verified that the PLM can better fit the fault data of the drum shearer cable system to analyze the reliability of the system . Other models of reliability evaluation that consider human factors are proposed, such as Petri‐net, hidden Markov models, Markov chain Monte Carlo method, and Bayes method . However, the above method does not consider the impact of working condition difference on the reliability of NC machine tools.…”

Section: Introductionmentioning

confidence: 99%

Application of power law model in reliability evaluation of machine tools by considering working condition difference

Zhu

et al. 2018

Quality & Reliability Eng

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The influence of working condition difference has not been considered in the traditional reliability modeling of numerical control (NC) machine tools. To solve the problem, a reliability evaluation method based on mixture variable parameter power law model (MVPPLM) is proposed in this study. First, the scale parameter of the PLM is obtained by multi‐dimensional exponential distribution. Second, a proportional relation of failure rate function between each working condition and reference working condition is established. The proportion coefficient is solved using the partial likelihood function. Working condition factors with a significant influence on reliability levels are selected through the chi‐squared test. Third, reliability evaluation models under different working condition levels are established through mixture distributions. The mixture weight coefficient is calculated by the standard deviation of working condition covariates. The maximum likelihood estimation method is used to estimate parameters. Finally, results of a case analysis based on the data of NC machine tools in the user field tracing test show that the MVPPLM has higher precision than the traditional method. Therefore, reliability evaluation that considers working condition difference is valuable for engineering application.

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“…In particular, residual useful life (RUL) estimation is a key technology for PHM. In general, RUL estimation is to indicate the system/component lifetime before it can no longer perform its function, which is also an important way to reduce production loss, save maintenance costs and avoid fatal machine breakdowns of the equipment before its failure [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…How to estimate RUL of an aircraft engine by utilizing these data has become the focus of most engine industries. Until now, approaches to predict system lifetime can be broadly categorized into three types: physics-based models, data-driven approaches and hybrid approaches [12][13][14]. Generally, a physics-based model utilizes the failure physical model of the When utilizing the data-driven approach for RUL estimation, the whole run-to-failure data of systems are normally needed, but it is difficult to obtain enough run-to-failure data for the long-life systems with high reliability.…”

Section: Introductionmentioning

confidence: 99%

“…How to utilize these deteriorated performance data, which do not work to failure, to estimate RUL of equipment by similarity-based method, is lacking and expected. Suspension history condition monitoring data usually contain useful information revealing the degradation situation of the system, including environmental factors and loading variations in actual situations, such as degradations and variations of stress amplitudes [10][11][12]34,35]. If these data are properly used, it is helpful to estimate RUL more accurately, particularly when the failure data are insufficient and unavailable in some cases [36,37].…”

Section: Introductionmentioning

confidence: 99%

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Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

2017

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Abstract:As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine's different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality.

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Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures

Cited by 42 publications

References 42 publications

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Application of power law model in reliability evaluation of machine tools by considering working condition difference

Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

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