Degradation modeling based on gamma process models with random effects

Rodríguez‐Picón, Luis Alberto; Rodríguez‐Picón, Anna Patricia; Méndez‐González, Luis Carlos; Rodríguez-Borbón, Manuel Iván; Alvarado‐Iniesta, Alejandro

doi:10.1080/03610918.2017.1324981

Cited by 21 publications

(18 citation statements)

References 21 publications

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“…The final is the degradation data of the crack propagation of a crack in a terminal of an electronic device provided by Rodríguez-Picón et al (2017). The data from 10 testing samples are collected and the crack growth for the terminal was measured every 0.1 hundred thousand cycles until 0.9 hundred thousand cycles.…”

Section: Examplementioning

confidence: 99%

A Stochastic Degradation Model Based on the Birnbaum-Saunders Distribution

Fujita

Nagatsuka

2021

TQS

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Products or systems degrade over time. Degradation over time is often modeled by stochastic processes to account for inherent randomness. Based on the assumption of additive accumulation of degradation, a family of degradation stochastic processes based on the Lévy process (the Wiener, gamma and inverse Gaussian (IG) processes are members of this family of processes) has been well studied in the literature. Recently, degradation stochastic model based on the generalized inverse Gaussian distribution was provided. This model is a generalization of the prominent existing degradation models in a sense, under the mild conditions. In this study, we propose a stochastic degradation model based on the Birnbaum-Saunders (BS) distribution. The BS distribution can be obtained as an approximation of an IG distribution. The BS distribution is proposed as the fatigue failure life distribution based on a physical consideration. The maximum likelihood estimation of the proposed model is also developed. Four case applications are performed to demonstrate the advantages of the proposed model, based on the well-known real degradation data sets of the GaAs laser devices and crack sizes of three kinds of specimens, and also a simulation study is conducted.

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Section: Examplementioning

confidence: 99%

A Stochastic Degradation Model Based on the Birnbaum-Saunders Distribution

Fujita

Nagatsuka

2021

TQS

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“…Gamma process is a nonnegative, stationary, and independently incremented stochastic process that can be used to describe continuous cumulative performance degradation processes, such as fatigue, rust, and creep. Therefore, Gamma process can be used to establish a state space model to describe the degradation process of VCSELs and achieve the prediction of pre-failure life [16].…”

Section: Degradation Failure Modelmentioning

confidence: 99%

Reliability Analysis of Vertical Cavity Surface-Emitting Lasers Based on the Gamma Process

Luo

2020

IEEE Access

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Due to the long life and high reliability of vertical cavity surface-emitting lasers, the performance degradation experiment cost is high, and the experiment cycle is long, because the degradation failure process of a semiconductor laser is a monotonic decreasing process, and the amount of performance degradation is independent for each function. Therefore, based on the Gamma process, the degradation performance of vertical cavity surface-emitting lasers has been modeled and analyzed in this paper. First, a model is given based on the Gamma process. Then, the failure distribution of the product is determined by analyzing the first arrival time distribution of the process, and then, the reliability of the product is statistically deduced to determine the residual life distribution of the device. Finally, combined with the data obtained from accelerated aging experiments, the product failure time of 261 470 hours is deduced, approximately 30 years. The model is in good agreement with the experimental results, which proves the validity of the model and provides a theoretical basis for the analysis of the reliability of the vertical cavity surface-emitting laser. According to the model, the reliability of the device decreases to 0.9994 after 100 000 hours of operation.

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“…Degradation over time is often modeled by a stochastic process {Y(t); t ≥ 0} to account for the damage and inherent randomness . Rodríguez‐Picón et al proposed a degradation model based on the Gamma process and considered that the random effects affect only the rate or the volatility of the process . Wang et al developed a performance degradation model based on the Wiener process to predict the remaining useful life (RUL) of a pump .…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11][12] Rodríguez-Picón et al proposed a degradation model based on the Gamma process and considered that the random effects affect only the rate or the volatility of the process. 13 Wang et al developed a performance degradation model based on the Wiener process to predict the remaining useful life (RUL) of a pump. 14 Liao and Elsayed extended an accelerated degradation testing (ADT) model to predict the field reliability of a product by considering stress variations in the field.…”

Section: Introductionmentioning

confidence: 99%

Engineering‐driven performance degradation analysis of hydraulic piston pump based on the inverse Gaussian process

Wang

Liao

et al. 2019

Quality & Reliability Eng

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As a key aircraft component, hydraulic piston pumps must be developed with high reliability. However, collecting failure time data of such pumps for reliability analysis is a big challenge. To save testing time, performance degradation data obtained from degradation tests can be used for quick reliability estimation of hydraulic piston pumps. This paper proposes an engineering‐driven performance degradation analysis method considering the nature of mechanical wear of hydraulic piston pumps. First, the failure mechanism of a type of hydraulic piston pump is investigated. By taking into account the close relationship between the degradation rate and the failure mechanism, an inverse Gaussian (IG) process model with a variable rate is developed to describe the degradation behavior of the pump. Under this model, a Bayesian statistical method is developed for degradation data analysis. The corresponding procedure for model parameter estimation and reliability evaluation is also presented. The proposed degradation analysis method is illustrated using a real experimental data. The results show that the engineering‐driven approach is quite effective in evaluating the lifetime of the hydraulic piston pump and will improve the overall reliability of aircraft operation in the field.

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Degradation modeling based on gamma process models with random effects

Cited by 21 publications

References 21 publications

A Stochastic Degradation Model Based on the Birnbaum-Saunders Distribution

A Stochastic Degradation Model Based on the Birnbaum-Saunders Distribution

Reliability Analysis of Vertical Cavity Surface-Emitting Lasers Based on the Gamma Process

Engineering‐driven performance degradation analysis of hydraulic piston pump based on the inverse Gaussian process

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