Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts
With the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary functio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/7/10/959 |
| _version_ | 1811261693456547840 |
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| author | Yuehjen E. Shao Shih-Chieh Lin |
| author_facet | Yuehjen E. Shao Shih-Chieh Lin |
| author_sort | Yuehjen E. Shao |
| collection | DOAJ |
| description | With the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary function of MSPC charts is to trigger an out-of-control signal when faults occur in a process. However, because two or more quality variables are involved in a multivariate process, it is very difficult to diagnose which one or which combination of quality variables is responsible for the MSPC signal. Though some statistical decomposition methods may provide possible solutions, the mathematical difficulty could confine the applications. This study presents a time delay neural network (TDNN) classifier to diagnose the quality variables that cause out-of-control signals for a multivariate normal process (MNP) with variance shifts. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments were conducted. The results were compared with artificial neural network (ANN), support vector machine (SVM) and multivariate adaptive regression splines (MARS) classifiers. It was found that the proposed TDNN classifier was able to accurately recognize the contributors of out-of-control signal for MNPs. |
| first_indexed | 2024-04-12T19:10:15Z |
| format | Article |
| id | doaj.art-7371b4e2690240629dcc4e3a4ca4da68 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-12T19:10:15Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7371b4e2690240629dcc4e3a4ca4da682022-12-22T03:19:54ZengMDPI AGMathematics2227-73902019-10-0171095910.3390/math7100959math7100959Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance ShiftsYuehjen E. Shao0Shih-Chieh Lin1Department of Statistics and Information Science, Fu Jen Catholic University Xinzhuang Dist., New Taipei City 24205, TaiwanDepartment of Statistics and Information Science, Fu Jen Catholic University Xinzhuang Dist., New Taipei City 24205, TaiwanWith the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary function of MSPC charts is to trigger an out-of-control signal when faults occur in a process. However, because two or more quality variables are involved in a multivariate process, it is very difficult to diagnose which one or which combination of quality variables is responsible for the MSPC signal. Though some statistical decomposition methods may provide possible solutions, the mathematical difficulty could confine the applications. This study presents a time delay neural network (TDNN) classifier to diagnose the quality variables that cause out-of-control signals for a multivariate normal process (MNP) with variance shifts. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments were conducted. The results were compared with artificial neural network (ANN), support vector machine (SVM) and multivariate adaptive regression splines (MARS) classifiers. It was found that the proposed TDNN classifier was able to accurately recognize the contributors of out-of-control signal for MNPs.https://www.mdpi.com/2227-7390/7/10/959time delay neural networksmultivariate normal processvariance shiftout-of-control signalsoft computing |
| spellingShingle | Yuehjen E. Shao Shih-Chieh Lin Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts Mathematics time delay neural networks multivariate normal process variance shift out-of-control signal soft computing |
| title | Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts |
| title_full | Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts |
| title_fullStr | Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts |
| title_full_unstemmed | Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts |
| title_short | Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts |
| title_sort | using a time delay neural network approach to diagnose the out of control signals for a multivariate normal process with variance shifts |
| topic | time delay neural networks multivariate normal process variance shift out-of-control signal soft computing |
| url | https://www.mdpi.com/2227-7390/7/10/959 |
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