A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis

Hu, Sheng; Zhao, Liping; Yao, Yiyong; Dou, Rushan

doi:10.1080/00207543.2016.1178862

Cited by 18 publications

(7 citation statements)

References 33 publications

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“…State space model [10,11] and error stream theory [12,13] were successively proposed to analyze quality errors accumulated in multistage process. With the development of complexity for production process and the improvement of the quality monitoring technology, many researches have been focused on the intelligent quality analysis technology [14], including the quality monitor [15], capability performance analysis [16], and the change point estimation in abnormal process [17,18]. Salah et al [19] established a surface quality evaluation and temperature monitoring model for the billet in continuous casting.…”

Section: Process Monitoring and Fluctuation Evaluationmentioning

confidence: 99%

A Multidimensional Data Flow Driven-Based Quality Fluctuation Evaluation for Manufacturing Process

Dong

2021

Mathematical Problems in Engineering

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Real-time evaluation quality fluctuation is an important way to ensure product quality in manufacturing process. This study proposes a multidimensional data flow driven-based quality fluctuation evaluation. Firstly, the multidimensional decomposition and transition mode analysis is carried out to reveal the essence of quality fluctuation. Then, the technique uses hierarchical network architecture to model the process, and quality fluctuation network is built to track fluctuation evolution trend; then, the node relative entropy measure is defined to statistically analyze the change of net-node state fluctuation. Finally, a case study is used to verify the proposed quality fluctuation evaluation approach.

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Section: Process Monitoring and Fluctuation Evaluationmentioning

confidence: 99%

A Multidimensional Data Flow Driven-Based Quality Fluctuation Evaluation for Manufacturing Process

Dong

2021

Mathematical Problems in Engineering

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“…e statistical quality control [10,11] and state-space model [12,13] were successively proposed to analyze fluctuation state and quality errors in production process. With the development of artificial intelligence and information technology and its application in the manufacturing field, domestic and foreign scholars constantly explore the application of data-driven methods in process quality control and improvement technology [14], including the quality monitoring [15], capability performance analysis [16], and the change point technology [17,18]. Yazid et al [19] established an adaptive support vector machine-based temperature monitoring and surface quality evaluation model in continuous casting process.…”

Section: Process Quality Modeling and Monitoringmentioning

confidence: 99%

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Shuanjun

Liu

2020

Mathematical Problems in Engineering

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Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.

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“…In the last decades, statistical process control (SPC) charts were the most popular tools to monitor the stability and variability of the industrial application [5][6][7][8]. The SPC charts have been utilized to identify either method is statistically under or out of control condition; however, the presence of autocorrelation and a specific pattern in the data cannot provide the possibility of correctly, quickly detecting, and classifying the existing fault [9][10][11]. It is crucial a quick detection of these shifts, and their causes for promoting required action at earlier stage of production [12,13].…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Investigation of Estimating Change Point in Multivariate Processes via Simultaneous Covariance Matrix and Mean Vector

et al. 2021

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The identification of change points in statistical process control (SPC) data is the critical criterion for multivariate techniques when output is out-of-control condition. Therefore, monitoring all independent variables is essential and demands targeted attention to avoid errors at the systems control stage. However, estimating change-point in multivariate control charts is the main problem when these correlated quality characteristics monitor together. Therefore, we proposed a combination of an ensemble learning-based model of artificial neural networks with support vector machines to monitor process mean vector and covariance matrix shifts simultaneously to estimate the change point in a multivariable system. The performance of the final model indicated an estimated changing point with one sample over 6,000 simulated cases with a probability of 98 percent, which is a significantly high accuracy rating. Finding suggests the outcome of the project confirms that the proposed model can provide a precise estimating the change point by monitoring the mean vector and the covariance matrix simultaneously and, helps to identify those variable(s) responsible for an out-of-control condition. For further validation of the model, the performance of the proposed model has been compared with previous reported which confirms a better performance of the proposed model. Finally, the model was applied to monitor the performance of the solar hydrogen production system and the model identify the variables which have negative effects on the performance of the system.

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A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis

Cited by 18 publications

References 33 publications

A Multidimensional Data Flow Driven-Based Quality Fluctuation Evaluation for Manufacturing Process

A Multidimensional Data Flow Driven-Based Quality Fluctuation Evaluation for Manufacturing Process

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Theoretical and Experimental Investigation of Estimating Change Point in Multivariate Processes via Simultaneous Covariance Matrix and Mean Vector

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