Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data

Qiu, Peihua; Xie, Xiulin

doi:10.1080/00401706.2021.1929493

Cited by 25 publications

(5 citation statements)

References 42 publications

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“…The additional advantages of our CCs include their ability to harness high dimensional, mixed-type data, their semiparametric nature, as well as the fast convergence and robustness of the algorithmic procedure. In a recent paper, Qiu and Xie 75 present an interesting discussion, and a fairly extensive literature review, associated with the application of recent Machine Learning (ML) methods to the area of SPC, citing recent reviews on CCs based on ML by Zhang et al, 76 Megahed and Jones-Farmer, 77 and Weese et al 78 Furthermore, Qiu and Xie 75 propose a general sequential learning framework for serially correlated data that is appropriate for continuous rvs. A closer look at the relevant literature of ML that are applied to SPC shows that again the existing methods are suitable for continuous rvs.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

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Multivariate semiparametric control charts for mixed-type data

Sofikitou

Markatou

Koutras

2023

Stat Methods Med Res

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A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

“…Our proposed method is for mixed-type data, a point of differentiation of our work from other works in the literature. An interesting question may be the extension of the transparent sequential learning framework and associated charts of Qiu and Xie 75 to mixed-type measurements.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Multivariate semiparametric control charts for mixed-type data

Sofikitou

Markatou

Koutras

2023

Stat Methods Med Res

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“…This latter assumption implies that the correlation between two process observations decreases gradually when their observation times get farther away and the serial correlation can be ignored if the two observation times are at least b max points away, which should be reasonable in practice. By the way, if the stationarity assumption is violated, then the alternative monitoring procedure discussed in Qiu and Xie (2022) can be considered.…”

Section: Online Monitoring Of a Dynamic Networkmentioning

confidence: 99%

Nonparametric online monitoring of dynamic networks

Wang,

Xie,

Qiu

2024

Journal of Quality Technology

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Network sequence has been commonly used for describing the longitudinal pattern of a dynamic system. Proper online monitoring of a network sequence is thus important for detecting temporal structural changes of the system. To this end, there have been some discussions in the statistical process control (SPC) literature to first extract some features from the observed networks and then apply an SPC chart to monitor the extracted features sequentially over time.However, the features used in many existing methods are insensitive to some important network structural changes, and the control charts used cannot accommodate the complex structure of the extracted features properly. In this paper, we suggest using four specific features to describe the structure of an observed network, and their combination can reflect most network structural changes that we are interested in detecting in various applications. After the four features are extracted from the observed networks, we suggest using a multivariate nonparametric control chart to monitor the extracted features online. Numerical studies show that our proposed network monitoring method is more reliable and effective than some representative existing methods in various cases considered.

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“…In the era of big data, it faces tremendous challenges, especially in the sparse strategies for monitoring networks and/or graphics data that are prevalent in diverse scenarios (Asikainen et al, 2020; Crane & Dempsey, 2018; Zhang et al, 2021). In this paper, we focus on the problem of online monitoring a serially correlated directed network (e.g., transport system), in which correlation undermines the assumption of conventional monitoring techniques and leads to unreliable performance (Qiu & Xie, 2022).…”

Section: Introductionmentioning

confidence: 99%

Robust online detection in serially correlated directed network

Zhou

Tsung

2023

Naval Research Logistics

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As the complexity of production processes increases, the diversity of data types drives the development of network monitoring technology. This paper mainly focuses on an online algorithm to detect serially correlated directed networks robustly and sensitively. First, we consider a transition probability matrix to resolve the double correlation of primary data. Further, since the sum of each row of the transition probability matrix is one, it standardizes the data, facilitating subsequent modeling. Then we extend the spring length based method to the multivariate case and propose an adaptive cumulative sum (CUSUM) control chart on the strength of a weighted statistic to monitor directed networks. This novel approach assumes only that the process observation is associated with nearby points without any parametric time series model, which is in line with reality. Simulation results and a real example from metro transportation demonstrate the superiority of our design.

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Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data

Cited by 25 publications

References 42 publications

Multivariate semiparametric control charts for mixed-type data

Multivariate semiparametric control charts for mixed-type data

Nonparametric online monitoring of dynamic networks

Robust online detection in serially correlated directed network

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