A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

El-Midany, T. T.; El-Baz, Maryam; Abd-Elwahed, Mohamed S.

doi:10.1016/j.eswa.2009.05.092

Cited by 76 publications

(30 citation statements)

References 10 publications

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“…El-Midany et al used ANNs to recognize a set of sub-classes of multivariate abnormal patterns [11] in machining of a crank case as one of the main components of a compressor. They used a simulated and a real world data set as well; furthermore they can identify the responsible variable(s) on the occurrence of the abnormal pattern.…”

Section: Production Trend Forecast Methodsmentioning

confidence: 99%

Production trend identification and forecast for shop-floor business intelligence

Viharos

Csanaki²,

Nacsa

et al. 2016

ACTA IMEKO

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<p>The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits.</p><p>The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology.</p>

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Section: Production Trend Forecast Methodsmentioning

confidence: 99%

Production trend identification and forecast for shop-floor business intelligence

Viharos

Csanaki²,

Nacsa

et al. 2016

ACTA IMEKO

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“…The other schemes based on fully ANN-based models as proposed in Zorriassatine, Tannocli, andO'Bricn (2003), C u h (2007). Yuand Xi (2009) andEl-Midany et al (2010) also can be classified as a single-stage monitoring scheme. In this research, two-stage monitoring scheme was investigated by integrating the powerful of MEWMA control chart and Synergistic-ANN model for improving the monitoring-diagnosis performance.…”

Section: Two-stage Intelligent Monitoring Schemementioning

confidence: 99%

Bivariate quality control using two-stage intelligent monitoring scheme

Masood¹,

Hassan²

2014

Expert Systems with Applications

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“…For trend abnormal state mode: (4) where t 0 represents occurrence of trend starting point,  represents trend slope,  represents trend abnormal coefficient.…”

Section: Control Chart Pattern Primitive Type and Recognition Principlementioning

confidence: 99%

Hybrid Patterns Recognition of Control Chart Based on WA-PCAPSO-SVM

Liu¹,

Zhang²,

Liu³

et al. 2014

IJCA

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A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

Cited by 76 publications

References 10 publications

Production trend identification and forecast for shop-floor business intelligence

Production trend identification and forecast for shop-floor business intelligence

Bivariate quality control using two-stage intelligent monitoring scheme

Hybrid Patterns Recognition of Control Chart Based on WA-PCAPSO-SVM

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