Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

Nishimura, Kazuhiko; Matsuura, Shinya; Suzuki, Hideo

doi:10.1016/j.spl.2015.05.003

Cited by 21 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Akaike information criterion (AIC), which is an information criterion based on the concept of entropy, can be used to select statistical models by evaluating the accuracy and complexity of the model [38,39]. In general, AIC must be combined with a logistic regression model to achieve feature selection.…”

Section: Feature Screening Based On Akaike Information Criterion (Aic) Methodsmentioning

confidence: 99%

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

et al. 2020

View full text Add to dashboard Cite

Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.

show abstract

Section: Feature Screening Based On Akaike Information Criterion (Aic) Methodsmentioning

confidence: 99%

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Multivariate EWMA CCs are proposed using multiple variables based on an AIC calculation for multivariate SPC [7]. The frequency of unblinded and blinded adverse events accumulating from single and multiple trials using CCs was monitored in [8].…”

Section: Introductionmentioning

confidence: 99%

Effect of Measurement Error on the Multivariate CUSUM Control Chart for燙ompositional Data

Imran¹,

Sun²,

Zaidi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Control charts (CCs) are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E's) are involved in the quality characteristic of interest, which can effect the CC's performance. The authors explored the impact of a linear model with additive covariate M.E on the multivariate cumulative sum (CUSUM) CC for a specific kind of data known as compositional data (CoDa). The average run length (ARL) is used to assess the performance of the proposed chart. The results indicate that M.E's significantly affects the multivariate CUSUM-CoDa CCs. The authors have used the Markov chain method to study the impact of different involved parameters using six different cases for the variance-covariance matrix (VCM) (i.e., uncorrelated with equal variances, uncorrelated with unequal variances, positively correlated with equal variances, positively correlated with unequal variances, negatively correlated with equal variances and negatively correlated with unequal variances). The authors concluded that the error VCM has a negative impact on the performance of the multivariate CUSUM-CoDa CC, as the ARL increases with an increase in the value of the error VCM. The subgroup size m and powering operator b positively impact the proposed CC, as the ARL decreases with an increase in m or b. The number of variables p also has a negative impact on the performance of the proposed CC, as the values of ARL increase with an increase in p. For the implementation of the proposal, two illustrated examples have been reported for multivariate CUSUM-CoDa CCs in the presence of M.E's. One deals with the manufacturing process of uncoated aspirin tablets, and the other is based on monitoring the machines involved in the muesli manufacturing process.

show abstract

“…In most cases, it needs to use relevant methods to extract the key influencing parameters for further evaluation of the component state. Among these data-driven methods, methods to achieve data dimensionality reduction include principal component analysis (PCA) [8]- [10], modified principal component analysis (MPCA) [11], partial least squares (PLS) [12], and independent component analysis (ICA) [13]- [14]. Among the methods mentioned above, PCA is the most widely used technique with a simple concept [15].…”

Section: Introductionmentioning

confidence: 99%

Thyristor State Evaluation Method Based on Kernel Principal Component Analysis

Lei

Guo

Feng³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the thyristor test data, operation records, maintenance history, appearance inspection information, states of other components and operating environment. A basic index system for evaluating the aging state of thyristor with 42 parameters is established. Next, a mathematical model was developed by Fisher Discriminant Analysis (FDA). The kernel function of the kernel principal components is then optimized by an improved particle swarm optimization (IPSO) algorithm. The improved KPCA is applied to extract key parameters from the base index system to obtain the reduced dimensional evaluation indicators. The obtained principal component factors are used to determine the weights of the fuzzy composite factors, which are applied for fuzzy evaluation of the thyristor. Finally, 20 thyristors are selected for experimental and theoretical calculations. The results show that the cumulative contribution of the first three principal component variables after dimensionality reduction reaches 93.76%, which is consistent with the state of the thyristor. Compared to the four existing evaluation methods, the results of the method proposed in this paper are more reasonable, which removes the influence of redundant indicators, reduces the amount of data, and provides a reference for the related research on thyristor state evaluation.

show abstract

Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

Cited by 21 publications

References 23 publications

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

Effect of Measurement Error on the Multivariate CUSUM Control Chart for燙ompositional Data

Thyristor State Evaluation Method Based on Kernel Principal Component Analysis

Contact Info

Product

Resources

About