Estimating Weibull Parameters for a General Class of Devices from Limited Failure Data

Balaban, Harold S.; Haspert, Kent

doi:10.1109/tr.1972.5215956

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Under the same conditions, the experimental data [27] are compared with the predicted results. The root mean square error (i.e., RMSE) is around 1%, as defined in Equation (40). As depicted in Figure 3b, the modeling outcomes closely match the empirical data.…”

Section: Grid Arrangement and Model Validationsupporting

confidence: 55%

“…This indicates the broad applicability of the Weibull distribution, which can be used for different types of equipment and systems, including SOFCs. There are various parameter estimation methods available, which are particularly important for SOFCs operated for a long period with only limited failure data [40]. Moreover, even with a small sample size, the Weibull distribution can provide reliable parameter estimations and reliability assessments.…”

Section: Failure Probability Analysismentioning

confidence: 99%

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Thermal Stress in Full-Size Solid Oxide Fuel Cell Stacks by Multi-Physics Modeling

Zhang,

Wu,

Xiao

et al. 2024

Energies

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Mechanical failures in the operating stacks of solid oxide fuel cells (SOFCs) are frequently related to thermal stresses generated by a temperature gradient and its variation. In this study, a computational fluid dynamics (CFD) model is developed and further applied in full-size SOFC stacks, which are fully coupled and implemented for analysis of heat flow electrochemical phenomena, aiming to predict thermal stress distribution. The primary object of the present investigation is to explore features and characteristics of the thermal stress influenced by electrochemical reactions and various transport processes within the stacks. It is revealed that the volume ratio of the higher thermal stress region differs nearly 30% for different stack flow configurations; the highest probability of potential failure appears in the cell cathodes; the more cells applied in the stack, the greater the difference in the predicted temperature/thermal stress between the cells; the counter-flow stack performs the best in terms of output power, but the predicted thermal stress is also higher; the cross-flow stack exhibits the lowest thermal stress and a lower output power; and although the temperature and thermal stress distributions are similar, the differences between the unit cells are bigger in the longer stacks than those predicted for shorter stacks. The findings from this study may provide a useful guide for assessing the thermal behavior and impact on SOFC performance.

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Section: Grid Arrangement and Model Validationsupporting

confidence: 55%

Section: Failure Probability Analysismentioning

confidence: 99%

Thermal Stress in Full-Size Solid Oxide Fuel Cell Stacks by Multi-Physics Modeling

Zhang,

Wu,

Xiao

et al. 2024

Energies

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“…The traditional failure rate prediction methods of substation equipment mainly can be divided into two methods: (1) One method is Weibull distribution function model based on Marquardt method [7]- [10], [22], which establishes Weibull distribution function model for failure rate data and carries out parameter estimation by Marquardt method. (2) The other method is to establish corresponding mathematical models.…”

Section: Compare With Other Methodsmentioning

confidence: 99%

“…At present, the relevant research organizations have carried out some researches in the failure rate prediction of substation equipment. One method is to establish Weibull distribution function model [7]- [10], and the other method is to establish corresponding mathematical models which include time series analysis, Gray system model, Artificial Neural Network, and so on [11]- [15]. In addition, [16] studies the non-linear and non-stationary characteristics of failure rate time series, and then decomposes the failure rate data into multiple parts to predict respectively.…”

Section: Introductionmentioning

confidence: 99%

Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis

Wang

Yin

2019

IEEE Access

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The failure rate is an important indicator to assess the reliability of substation equipment, and the failure rate prediction is an effective way to master the operation status of substation equipment. According to the characteristics of failure rate data, such as failure period segmentation and stochastic variation, this paper proposes a combined prediction method by the data decomposition of failure rate time series and the segmentation theory of failure periods and then establishes a new failure rate prediction model of the substation equipment based on the Weibull distribution and time series analysis. Compared with the traditional Weibull distribution function model which cannot describe the stochastic variation of failure rate data and cannot identify the failure period automatically, the proposed model in this paper uses the minimum sum algorithm of residual squares obtained by the Weibull distribution to accurately identify the demarcation point between different failure periods and then establishes the autoregressive moving average (ARMA) model to achieve combined prediction. Finally, the effectiveness of the proposed model is verified by the engineering example and comparison with the traditional failure rate prediction methods. INDEX TERMS Equipment failure, predictive models, Weibull distribution, time series analysis.

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Good ridge estimators based on prior information

Swindel

1976

Communications in Statistics - Theory and Methods

131

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Estimating Weibull Parameters for a General Class of Devices from Limited Failure Data

Cited by 4 publications

References 5 publications

Thermal Stress in Full-Size Solid Oxide Fuel Cell Stacks by Multi-Physics Modeling

Thermal Stress in Full-Size Solid Oxide Fuel Cell Stacks by Multi-Physics Modeling

Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis

Good ridge estimators based on prior information

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