Unaligned Profile Monitoring Using Penalized Methods

Zang, Yangyang; Wang, Kaibo; Jin, Ran

doi:10.1002/qre.2066

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The delicately controlled temperature gradient between the solid‐liquid interface, pulling speed of the seed, rotating speed of the crucible, power of the heating system, and other components of the complex system collaborate carefully to guarantee the steady growth of the silicon ingot. Depending on the amount of raw materials invested, each growth cycle may span from 1 day to 1 week 1 …”

Section: Introductionmentioning

confidence: 99%

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Imputation of continuous missing values in profile data

Luo

Wang

2022

Quality & Reliability Eng

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Profile data have been widely used in quality control practice. However, in some applications, incomplete profiles, or profiles with continuous missing values, are frequently encountered. Imputation of continuous missing values in profiles for quality control is a challenging problem. In this work, we propose a latent feature model and a two-step learning algorithm to reconstruct missing values. In the model, an unsupervised deep-learning technique, variational autoencoder (VAE), is utilized to learn the latent features from profile data. Then a supervised variational coding (SVC) scheme is proposed to map the incomplete profile data to the learned latent features, and imputation is then made based on the latent features. The performance of the proposed method is evaluated by simulated and real datasets, and the results prove the effectiveness and superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

“…Depending on the amount of raw materials invested, each growth cycle may span from 1 day to 1 week. 1…”

Section: Introductionmentioning

confidence: 99%

Imputation of continuous missing values in profile data

Luo

Wang

2022

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

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Special Issue on INFORMS 2015 Annual Meeting

Chen

Jeong

2016

Quality & Reliability Eng

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E ach year, the Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting provides an excellent platform for researchers world-wide to present their state-of-the-art research in quality and reliability. The objective of this Special Issue is to introduce the recent advancements in quality and reliability methodological studies as well as applications in a form of full-length papers expanded from the presentations in the INFORMS 2015 Annual Meeting (November 1-4, Philadelphia, PA).We are very grateful for the contributions of the authors who submitted papers. After two to three rounds of rigorous reviews, 10 papers were accepted for publication. Among them, five papers focus on process monitoring and fault identification. The paper by Turkoz et al. proposes a new fault identification method when a high dimensional process is out-of-control. The proposed method combines the support vector data description-based test statistic with an adaptive step-down procedure to identify the faults. By using a nonparametric one-class classification method, the proposed approach does not rely on any distribution assumption. Compared with the existing distribution free methods, the proposed method has much more stable performance when the number of faults is more than one. The paper by Choe et al. focuses on the off-line change-point detection problem for time-series data. It adopts the Thresholded Least Absolute Shrinkage and Selection Operator (LASSO) techniques to control the false positives. The authors demonstrated the superior performance of the proposed method by comparing with several benchmark methods based on both simulations and a case study related to solar panel performance. The paper proposed by Zang, Wang, and Jin is handling with the issue of monitoring of processes based on unaligned profiles. While existing works focus on monitoring of well-aligned profiles, this paper develops new algorithms for monitoring unaligned profiles with varying sampling points. For this, they propose a robust dynamic time warping algorithm for profile alignment that are robust to noises and shift signals. And then, they propose a penalization-based charting algorithm that gives more effective performance in shift detection. In order to illustrate their proposed framework, they applied it for unaligned profile monitoring to the ingot growth process and monitoring heating power profiles. The paper by Abdella et al. proposes the double Exponentially Weighted Moving Average based procedure to evaluate the quality of a process based on polynomial quality profiles. The simulations studies have revealed the distinctive performance of their proposed double Exponentially Weighted Moving Average based control charts in quickly detecting changes in the second-order polynomial profiles. Their extensive simulation studies are based on two shift patterns in the polynomial quality profiles: changes in the coefficients of the regression parameters and changes in the process variability. Timely detection of whether a data ...

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