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Comparing Niaki [36] model and proposed model 

Comparing Niaki [36] model and proposed model 

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Table 1: Detail specifications for the ANNs 
Figure 1: Main modules and secondary modules
Table 3: Number of examples for networks D-G 
Table 4: Comparing Niaki [36] model and proposed model 
A comprehensive scheme to monitor simultaneously all required knowledge for an effective root cause analysis
Conference Paper
Full-text available
  • Jan 2011
When a control chart indicates that a sustained disturbance has manifested itself to the process, practitioners begin a root cause analysis to find and eliminate the disturbance source. In the case of multivariate process, practitioners could experience an effective root cause analysis when a model leads them to identify four main knowledge includi...
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Context 1
... concept is considered to evaluate the performance of the proposed model. Table 4 shows the results obtained from the proposed model and the results obtained from Niaki and abbasi [36] model in term of error rate percentage. Error rate percentage is calculated as: ER%=1-correct classification percentage (6) As shown in Table 4, the diagnostic analysis capability of Niaki and abbasi [36] model which they reported on table I of their paper is very weaker than the capability of the proposed model. ...
Context 2
... 4 shows the results obtained from the proposed model and the results obtained from Niaki and abbasi [36] model in term of error rate percentage. Error rate percentage is calculated as: ER%=1-correct classification percentage (6) As shown in Table 4, the diagnostic analysis capability of Niaki and abbasi [36] model which they reported on table I of their paper is very weaker than the capability of the proposed model. Also, Niaki and abbasi [36] model is not a comprehensive model, because it is incapable of detecting the out-of-control condition, identifying the change point and distinguishing shift direction for an out-of-control process. ...

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Citations

... They reported comprehensively the specifications of the interesting proposed model along with the results of its performance under a simulated data and a case study data corresponding to a car manufacturing line. Noorossana et al. [48] claimed their proposed model is superior compared with the model proposed by Atashgar and Noorossana [64]. The Atashgar and Noorossana [64] model is capable to perform identifying the out-of-control condition, the change point, the source(s) of the shift, and the shift direction, all at the same time when the mean vector affecting a step change type shifts the process to an out-of-control condition. ...
... Noorossana et al. [48] claimed their proposed model is superior compared with the model proposed by Atashgar and Noorossana [64]. The Atashgar and Noorossana [64] model is capable to perform identifying the out-of-control condition, the change point, the source(s) of the shift, and the shift direction, all at the same time when the mean vector affecting a step change type shifts the process to an out-of-control condition. Aparisi et al. [65] also considered the diagnostic analysis arena as a considerable attention in MSPC and proposed an ANN based model. ...
Monitoring Multivariate Environments Using Artificial Neural Network Approach: An Overview
Article
  • Dec 2015
  • SCI IRAN
When a process shifts to an out-of-control condition, a search should be initiated to identify and eliminate the special cause(s) manifested to the technical speci�cation(s) of the process. In the case of a process (or a product) involving several correlated technical speci�cations, analyzing the joint e�ects of the correlated speci�cations is more complicated compared to a process involving only one technical speci�cation. Most real cases refer to processes involving more than one variable. The complexity of a solution to monitor the condition of these processes, estimate the change point and identify further knowledge leading to root-cause analysis motivated researchers to develop solutions based on Arti�cial Neural Networks (ANN). This paper provides, analytically, a comprehensive literature review on monitoring multivariate processes approaching arti�cial neural networks. Analysis of the strength and weakness of the proposed schemes, along with comparing their capabilities and properties,, are also considered. Some opportunities for new researches into monitoring multivariate environments are provided in this paper
View
... They reported comprehensively the speci cations of the interesting proposed model, along with the results of its performance under simulated data, and a case study data corresponding to a car manufacturing line. Noorossana et al. [48] claimed their proposed model is superior compared with the model proposed by Atashgar and Noorossana [64]. The Atashgar and Noorossana [64] model is capable of identifying the out-of-control condition, the change point, the source(s) of the shift, and the shift direction, all at the same time, when the mean vector a ecting a step change type shifts the process to an out-ofcontrol condition. ...
... Noorossana et al. [48] claimed their proposed model is superior compared with the model proposed by Atashgar and Noorossana [64]. The Atashgar and Noorossana [64] model is capable of identifying the out-of-control condition, the change point, the source(s) of the shift, and the shift direction, all at the same time, when the mean vector a ecting a step change type shifts the process to an out-ofcontrol condition. Aparisi et al. [65] also considered the diagnostic analysis arena with considerable attention in MSPC and proposed an ANN based model. ...
... The subinterval was approached rst by Atashgar [76] when he undertook his PhD dissertation. The e ectiveness of the procedure is examined by Atashgar and Noorossana [47,63,64,66,77] and Noorossana et al. [48] for several models. The subinterval approach is commented upon, especially when a multivariate process with correlation random variables is studied. ...
Monitoring multivariate environments using artificial neural network approach: An overview
Data
Full-text available
  • Jan 2015
When a process shifts to an out-of-control condition, a search should be initiated to identify and eliminate the special cause(s) manifested to the technical specification(s) of the process. In the case of a process (or a product) involving several correlated technical specifications, analyzing the joint effects of the correlated specifications is more complicated compared to a process involving only one technical specification. Most real cases refer to processes involving more than one variable. The complexity of a solution to monitor the condition of these processes, estimate the change point and identify further knowledge leading to root-cause analysis motivated researchers to develop solutions based on Artificial Neural Networks (ANN). This paper provides, analytically, a comprehensive literature review on monitoring multivariate processes approaching artificial neural networks. Analysis of the strength and weakness of the proposed schemes, along with comparing their capabilities and properties,, are also considered. Some opportunities for new researches into monitoring multivariate environments are provided in this paper.
View