Stochastic Evaluation of Magnetic Head Wears in Hard Disk Drives

Yu, Wenbin; Ye, Zhi‐Sheng; Tsui, Kwok Leung

doi:10.1109/tmag.2013.2293636

Cited by 31 publications

(10 citation statements)

References 35 publications

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“…In the literature, Jin et al [156] utilized a modified Wiener process to calculate the RUL of secondary batteries, whereas Tsai [2] employed the Wiener diffusion model to analyze lumen degradation data. Besides, Wiener process-based methods have been adopted for reliability assessment of mechanical structures, e.g., metal fatigue [157], [158], head wears [159], momentum wheels [160], and pumps [161]. Unfortunately, the conventional Wiener process only concentrates on the current degradation data, while other available information during the entire sequence of observations is neglected.…”

Section: A: Wiener Processmentioning

confidence: 99%

“…Under this circumstance, random-effect Wiener processes are proposed to deal with these unobserved heterogeneities. A general way of capturing individual differences in the Wiener process is assuming the drift or diffusion coefficient to follow a certain distribution [159], [167], [173]. In the literature, Ye et al [167] compared the goodness-of-fitting of four different types of Wiener process-based models, namely, (1) the simple Wiener process; (2) the RD-Wiener model, in which the drift coefficient is randomly-distributed; (3) the RV-Wiener model where the diffusion coefficient is treated as a random variable; and (4) the RDV-Wiener model, in which both the drift and diffusion coefficient are normally-distributed variables.…”

Section: A: Wiener Processmentioning

confidence: 99%

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Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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Reliability engineering plays an important role in the design, manufacture, maintenance, and replacement of industrial products. Over the last few decades, accelerated degradation testing (ADT) has been largely utilized to shorten test durations, reduce the samples needed, and provide sufficient degradation data to ensure the effective reliability assessment of the concerned products. Meanwhile, performance degradation modeling has been recognized as an essential approach to help researchers and producers understand the health conditions of the deteriorating systems. However, the diversity in reliability tests, degradation models, and statistical analysis techniques has increased the difficulty in selecting appropriate reliability assessment methods in specific scenarios. Besides, there are no systematic reviews focused on modeling and analysis of performance degradation data. Therefore, this paper aims to (1) present ADT fundamentals, including the basic theory, ADT methods, accelerated stress variables, type of acceleration models, as well as ADT optimization, (2) comprehensively review current states and future challenges in degradation modeling, (3) discuss the problem of model mis-specification and compare different approaches for parameter estimation, (4) highlight future opportunities and possible directions deserving further research.

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Section: A: Wiener Processmentioning

confidence: 99%

Section: A: Wiener Processmentioning

confidence: 99%

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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“…Doksum and Hóyland assumed that degradation of a cable insulation follows the Wiener process and then failure data can be analysed using the IG distribution. Park and Padgett used the model to fit fatigue data of metals Wang et al , used the model to fit the head wear data of hard disk drives. The basic model provides a very useful basis for degradation analysis.…”

Section: Wiener Process Modelsmentioning

confidence: 99%

Stochastic modelling and analysis of degradation for highly reliable products

Xie

2014

Appl Stoch Models Bus & Ind

561

240

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Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision-makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn-in modelling will also be discussed.

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“…Over the past years, researchers were making efforts to develop more flexible models for directly describing the nonlinear degradation through a stochastic process [29][30][31][32][33]. More recent works can be referred to the publications of Si et al [34,35], Wang et al [36], Wang et al [37,38], Wang et al [39], Huang et al [40], Chiang et al [41], . In these papers, researchers have > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 modeled nonlinear degradation by using time-scaling techniques [37][38][39]41], nonlinear drift coefficient [29][30][31][32][33][34], or an adaptive parameter which can be updated dynamically [35,36,40].…”

mentioning

confidence: 99%

“…More recent works can be referred to the publications of Si et al [34,35], Wang et al [36], Wang et al [37,38], Wang et al [39], Huang et al [40], Chiang et al [41], . In these papers, researchers have > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 modeled nonlinear degradation by using time-scaling techniques [37][38][39]41], nonlinear drift coefficient [29][30][31][32][33][34], or an adaptive parameter which can be updated dynamically [35,36,40]. While these models have only been developed for the degradation process in CSADT, there are few studies for the SSADT modeling, which use stochastic models to describe nonlinear degradation processes [27,42].…”

mentioning

confidence: 99%

Lumen degradation modeling of white-light LEDs in step stress accelerated degradation test

Huang

Golubović²,

Koh

et al. 2016

Reliability Engineering & System Safety

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In this paper, lumen degradation is described by using a modified Brownian motion process for mid-power white-light LED packages, which were aged under step stress accelerated degradation test (SSADT). First, a SSADT model has been established based on the theory of equivalent accumulative damage. Then, a method was proposed to improve the accuracy of the parameter estimation by carefully modifying the estimator, which was proposed in the previous research. Experimental data show that parameters estimated by using SSADT model are very close to those estimated by using constant stress accelerated degradation test (CSADT) model, indicating the feasibility of the SSADT model. The experiment also indicates that SSADT can be used as an alternative to CSADT, as it enables comparable estimation accuracy, while using less testing time, a smaller sample size and less test capacity. Index Terms-accelerated test; Brownian motion; degradation test; light-emitting diodes; step stress; Wiener process I. INTRODUCTION ID-POWER white-light LEDs (MP-LEDs) have been widely considered as the new generation of luminaires due to their advantages of higher efficiency, longer lifetime, and more environment protections as compared to currently widely used lighting solutions (incandescence, fluorescence). Recently, due to fast evolution of the technologies, the lifetime of LED packages has been claimed to be as high as 50,000 hours. For such kind of products, failed units would be seldom observed during life tests, even though they are subjected to accelerated conditions. Therefore, it is unreasonable to collect Manuscript received

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Stochastic Evaluation of Magnetic Head Wears in Hard Disk Drives

Cited by 31 publications

References 35 publications

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Stochastic modelling and analysis of degradation for highly reliable products

Lumen degradation modeling of white-light LEDs in step stress accelerated degradation test

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