Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

Dombi, József; Jónás, Tamás; Tóth, Zsuzsanna

doi:10.1002/qre.1815

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Hence, multi-view clustering is widely applied for multi-view data processing. Besides, it has also attracted considerable attention on internet of things [1], smart home [2], electronic product analysis [3] in field of electronic consumption.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing

Pan

Che

et al. 2024

IEEE Trans. Consumer Electron.

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Multi-view data processing is an effective tool to differentiate the levels of consumers on electronics. Recently, the graph based multi-view clustering methods have attracted widespread attention because they can obtain the relationships of multi-view data points efficiently. However, there exist several shortcomings on most existing graph based clustering methods. Firstly, the mostly adopted Euclidean distance can not extract the nonlinear manifold structure. Secondly, graph based methods are mainly hard clustering methods, which means that each data point belongs to only the one cluster exactly. Thirdly, the highdimension information between multiple views are not taken into account. Thus, a low-rank tensor regularized graph fuzzy learning (LRTGFL) method for multi-view data processing is proposed. In LRTGFL, Jensen-Shannon divergence is adopted to replace the Euclidean distance for obtaining more completely nonlinear structures. In addition, fuzzy learning is adopted to make graph clustering be a soft clustering method. Furthermore, a tensor nuclear norm based on the tensor singular value decomposition (t-SVD) is adopted to take advantage of the highdimension information. Then, alternating direction method of multipliers (ADMM) is adopted to solve the LRTGFL model. Finally, the effectiveness and superiority of LRTGFL are demonstrated by comparing with various state-of-the-art algorithms on eight real-world datasets.

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Section: Introductionmentioning

confidence: 99%

Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing

Pan

Che

et al. 2024

IEEE Trans. Consumer Electron.

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“…As mentioned in remark 2 of Lai and Xie (2006), a constant in the middle regime of bathtub-shaped functions leads to a more general and yet more realistic description of failure rate functions. It is also proposed in section 3.4.5 of Lai and Xie (2006) that only a sectional model appears to achieve a constant in the middle regime of bathtub-shaped functions; see Dombi, Jónás, and Tóth (2016) and Jónás, Árva, and Tóth (2018) for examples. In these applications, an applicable interval within the two thresholds can help, for example, engineers conserve energy in industrial processes or medical personnel manage risk by reducing or increasing patient doses.…”

Section: Introductionmentioning

confidence: 99%

Threshold estimation for continuous three‐phase polynomial regression models with constant mean in the middle regime

Chang

Wong

Lim

2022

Statistica Neerlandica

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This paper considers a continuous three‐phase polynomial regression model with two threshold points for dependent data with heteroscedasticity. We assume the model is polynomial of order zero in the middle regime, and is polynomial of higher orders elsewhere. We denote this model by ℳ2$$ {\mathcal{M}}_2 $$, which includes models with one or no threshold points, denoted by ℳ1$$ {\mathcal{M}}_1 $$ and ℳ0$$ {\mathcal{M}}_0 $$, respectively, as special cases. We provide an ordered iterative least squares (OiLS) method when estimating ℳ2$$ {\mathcal{M}}_2 $$ and establish the consistency of the OiLS estimators under mild conditions. When the underlying model is ℳ1$$ {\mathcal{M}}_1 $$ and is false(d0prefix−1false)$$ \left({d}_0-1\right) $$th‐order differentiable but not d0$$ {d}_0 $$th‐order differentiable at the threshold point, we further show the Opfalse(Nprefix−1false/false(d0+2false)false)$$ {O}_p\left({N}^{-1/\left({d}_0+2\right)}\right) $$ convergence rate of the OiLS estimators, which can be faster than the Opfalse(Nprefix−1false/false(2d0false)false)$$ {O}_p\left({N}^{-1/\left(2{d}_0\right)}\right) $$ convergence rate given in Feder when d0≥3$$ {d}_0\ge 3 $$. We also apply a model‐selection procedure for selecting ℳκ$$ {\mathcal{M}}_{\kappa } $$; κ=0,1,2$$ \kappa =0,1,2 $$. When the underlying model exists, we establish the selection consistency under the aforementioned conditions. Finally, we conduct simulation experiments to demonstrate the finite‐sample performance of our asymptotic results.

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“…By contrast, the Weibull distribution can match the bathtub curve well, so that it has been widely used. Nevertheless, the Weibull distribution only pays attention to the effect of equipment running enlistment age on the failure rate, which ignores the effect of some external factors such as the equipment maintenance on the equipment failure rate [6]. Accordingly, Weibull distribution has some limitations.…”

Section: Introductionmentioning

confidence: 99%

Generalized Proportional Model of Relay Protection Based on Adaptive Homotopy Algorithm Transient Stability

Zheng¹,

Lin

Huang

et al. 2019

Processes

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Relay protection equipment is important to ensure the safe and stable operation of power systems. The risks should be evaluated, which are caused by the failure of relay protection. At present, the fault data and the fault status monitoring information are used to evaluate the failure risks of relay protection. However, there is a lack of attention to the information value of monitoring information in the normal operation condition. In order to comprehensively improve monitoring information accuracy and reduce, a generalized proportional hazard model (GPHM) is established to fully exploit the whole monitoring condition information during the whole operation process, not just the monitoring fault condition data, with the maximum likelihood estimation (MLE) used to estimate the parameters of the GPHM. For solving the nonlinear equation in the process of parameter estimations, the adaptive homotopy algorithm is adopted, which could ensure the reversibility of the Jacobi matrix. Three testing cases have been reviewed, to demonstrate that the adaptive homotopy algorithm is better than traditional algorithms, such as the Newton homotopy algorithm, regarding the calculation speed and convergence. Therefore, GPHM could not only reflect the real time state of the equipment, but also provide a sound theoretical basis for the selection of equipment maintenance types.

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Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

Cited by 7 publications

References 42 publications

Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing

Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing

Threshold estimation for continuous three‐phase polynomial regression models with constant mean in the middle regime

Generalized Proportional Model of Relay Protection Based on Adaptive Homotopy Algorithm Transient Stability

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