Functional Statistics: Outliers Detection and Quality Control, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2349

Special Issue Editor


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Guest Editor
Department of Applied Mathematics I, University of Vigo, Vigo, Spain
Interests: machine learning techniques: applications and new algorithms; functional statistics: outliers detection and quality control; image processing;
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Special Issue Information

Dear Colleagues,

At present, a large amount of data can be approached from the functional prism and in a multitude of fields such as engineering, medicine, etc. An example of this, which is very important to the engineering field (mechanical, electronic, environmental, etc.), is quality control, which is based on the classical Schewart methodology or WECO rules.

However, while application is important, a comparison between methods, and the design and construction of a new model, univariable or multivariable, based on depth, non-parametric, etc., is also important. Thus, in this Special Issue, different articles are collected with new models of detection of functional outliers, or applications thereof, on different areas of quality control and process capability control.

Prof. Dr. Javier Martínez
Guest Editor

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Keywords

  • Functional outliers
  • Functional depth
  • SPC
  • Capability
  • Control process

Published Papers (2 papers)

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Research

14 pages, 443 KiB  
Article
Sparsity-Constrained Vector Autoregressive Moving Average Models for Anomaly Detection of Complex Systems with Multisensory Signals
by Meng Ma, Zhongyi Zhang, Zhi Zhai and Zhirong Zhong
Mathematics 2024, 12(9), 1304; https://doi.org/10.3390/math12091304 - 25 Apr 2024
Viewed by 338
Abstract
Detecting anomalies in large, complex systems is a critical and challenging task, and this is especially true for high-dimensional anomaly detection due to the underlying dependency structures among sensors. To incorporate the interrelationships among various sensors, a novel sparsity-constrained vector autoregressive moving average [...] Read more.
Detecting anomalies in large, complex systems is a critical and challenging task, and this is especially true for high-dimensional anomaly detection due to the underlying dependency structures among sensors. To incorporate the interrelationships among various sensors, a novel sparsity-constrained vector autoregressive moving average (scVARMA) model is proposed for anomaly detection in complex systems with multisensory signals. This model aims to leverage the inherent relationships and dynamics among various sensor readings, providing a more comprehensive and accurate analysis suitable for complex systems’ complex behavior. This research uses convex optimization to search for a parameterization that is sparse based on the principal of parsimony. This sparse model will not only contribute to meeting the real-time requirements of online monitoring strategies but also keeps the correlations among different sensory signals. The performance of the proposed scVARMA model is validated using real-world data from complex systems. The results affirm the superiority of the proposed scheme, demonstrating its enhanced performance and potential in practical applications. Full article
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15 pages, 523 KiB  
Article
Asymmetric Control Limits for Weighted-Variance Mean Control Chart with Different Scale Estimators under Weibull Distributed Process
by Jing Jia Zhou, Kok Haur Ng, Kooi Huat Ng, Shelton Peiris and You Beng Koh
Mathematics 2022, 10(22), 4380; https://doi.org/10.3390/math10224380 - 21 Nov 2022
Viewed by 1181
Abstract
Shewhart charts are the most commonly utilised control charts for process monitoring in industries with the assumption that the underlying distribution of the quality characteristic is normal. However, this assumption may not always hold true in practice. In this paper, the weighted-variance mean [...] Read more.
Shewhart charts are the most commonly utilised control charts for process monitoring in industries with the assumption that the underlying distribution of the quality characteristic is normal. However, this assumption may not always hold true in practice. In this paper, the weighted-variance mean charts are developed and their population standard deviation is estimated using the three subgroup scale estimators, namely the standard deviation, median absolute deviation and standard deviation of trimmed mean for monitoring Weibull distributed data with different coefficients of skewness. This study aims to compare the out-of-control average run length of these charts with the pre-determined fixed value of the in-control ARL in terms of different scale estimators, coefficients of skewness and sample sizes via extensive simulation studies. The results indicate that as the coefficients of skewness increase, the charts tend to detect the out-of-control signal more rapidly under identical magnitude of shift. Meanwhile, as the size of the shift increases under the same coefficient of skewness, the proposed charts are able to locate the shifts quicker and the similar scenarios arise as a sample size raised from 5 to 10. A real data set from survival analysis domain which, possessing Weibull distribution, was to demonstrate the usefulness and applicability of the proposed chart in practice. Full article
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