A new on-line predictive monitoring using an integrated approach adaptive filter and PCA

Salahshoor, Karim; Alaei, Hesam Komari; Alaei, Hamed Komari

doi:10.1109/sofa.2010.5565615

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…To consider the characteristic of MMS, Cascade Quality Prediction Method (CQPM) [7,10] is implemented to mine the hidden relationship. Figure 2 shows the scenario of MMS and idea of CQPM.…”

Section: Model Building Based On Classificationmentioning

confidence: 99%

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

et al. 2017

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Abstract. For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart Cycle Deming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC) methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc.) can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA) with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.

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“…To consider the characteristic of MMS, Cascade Quality Prediction Method (CQPM) [7,10] is implemented to mine the hidden relationship. Figure 2 shows the scenario of MMS and idea of CQPM.…”

Section: Model Building Based On Classificationmentioning

confidence: 99%

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

et al. 2017

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“…As for the VIP limit, it was set to make sure the most insensitive variables not involved in the reconstruction of model. It is obvious in Figure 9 that most of variables were selected (variable 1, 2, 4, 5,7,8,10,13,14), whereas five variables were selected with lower frequency. Note that the third variable was considered as an important input variable for the soft-sensor modeling under wet weather but ignorable for the dry weather condition.…”

Section: Scenario Definitionmentioning

confidence: 99%

“…Soft-sensors are widely used to estimate variables that are difficult to measure online because of technical difficulty, large measurement delays, high investment cost, and so on. − To build a proper relationship between easy-to-measure variables x and those that are hard-to-measure y , statistical methods including, but not limited to, partial least-squares (PLS), Principle Component Regression (PCR), nonlinear PLS, and support vector machine based regression are researched as the soft-sensor models. One of the bottlenecks limiting their widespread applications is that their prediction performance sometimes deteriorates due to the highly varying operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Adaptive Soft-Sensor Using Variational Bayesian PLS with Accounting for Online Identification of Key Variables

Liu

Pan

Huang

2014

Ind. Eng. Chem. Res.

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Soft-sensor is the most common strategy to estimate the hard-to-measure variables in the chemical processes. Recent research has shown that accurate prediction of hard-to-measure variables can significantly improve system performance. However, deterioration of predictive ability resulting from dramatic changes in the operation conditions always renders a generic soft-sensor inadequate. This study developed an adaptive soft-sensor with Moving Window and Time Differencing technique accounting for both of long-term and short-term information for modeling. At each step of model update, the most insensitive variables were removed by VIP (Variable importance in projection). With further integrating Variational Bayesian PLS (VBPLS) as predictive model, not just prediction values are obtained but also the credibility of information for hard-to-measure quantities can be generated. The proposed methodology was first demonstrated by applying the design algorithm to a WWTP simulated with the well-established model, BSM1, then extended to a real WWTP with data collecting from the field. Results showed that the proposed strategy significantly improved the prediction performance.

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“…PCA has been widely used as a method to extracting relevant information from a confusing datasets [28]. Hence it is useful to find latent pattern in high dimensional data [29]. In quality prediction, PCA is used to define the new set of variables by transforming several correlated manufacturing operation variables.…”

Section: Related Workmentioning

confidence: 99%

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

Arif¹,

Suryana²,

Hussin³

2013

IJCA

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Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes. General TermsData Mining, Prediction Model.

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A new on-line predictive monitoring using an integrated approach adaptive filter and PCA

Cited by 4 publications

References 7 publications

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

Development of a Novel Adaptive Soft-Sensor Using Variational Bayesian PLS with Accounting for Online Identification of Key Variables

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

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