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An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

Barghash M - Comput Intell Neurosci (2015)

Bottom Line: Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble.In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity.It also can be used to discover even small shifts in the mean as early as possible.

View Article: PubMed Central - PubMed

Affiliation: IE Department, The University of Jordan, Amman 11942, Jordan.

ABSTRACT
Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

No MeSH data available.


Traditional 3-sigma level ARL curve which serves as a benchmark for comparison.
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fig2: Traditional 3-sigma level ARL curve which serves as a benchmark for comparison.

Mentions: Shift patterns are some of the most common assignable cause patterns. These occur when the process mean changes due to assignable causes. This is usually reflected in product characteristic changes such as size, shape, or other quality parameters. A usual measure for the success of SPC system is the average run length where the number of signal samples before an alarm is sounded and the process is out of control. Figure 2 shows a sample ARL chart which is generated based on the basic benchmark traditional X-bar. The x-axis is the shift size. There is no shift at the origin or point zero. When the shift size is small, close to the origin, there will be some mix-up between shift and no shift case, while at large shift case, far from the origin, there is usually no mix-up with no shift case. The ARL at zero shift size is close to 373; that is, on average, a false alarm is issued every 373 data points. For the case of small shift 0.5 standardized shift (the system mistakenly classifies this as no shift for a large number of samples), nearly 150 data points are passed before the shift is discovered. A better performing system may reduce the number of false alarms while keeping the 0.5 standard shift at 150, or keeping the ARL at zero shift to 373 while reducing the ARL for 0.5 sigma to less than 150, or it can increase the ARL at zero shift and reduces the ARL at 0.5 sigma to less than 150.


An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

Barghash M - Comput Intell Neurosci (2015)

Traditional 3-sigma level ARL curve which serves as a benchmark for comparison.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538969&req=5

fig2: Traditional 3-sigma level ARL curve which serves as a benchmark for comparison.
Mentions: Shift patterns are some of the most common assignable cause patterns. These occur when the process mean changes due to assignable causes. This is usually reflected in product characteristic changes such as size, shape, or other quality parameters. A usual measure for the success of SPC system is the average run length where the number of signal samples before an alarm is sounded and the process is out of control. Figure 2 shows a sample ARL chart which is generated based on the basic benchmark traditional X-bar. The x-axis is the shift size. There is no shift at the origin or point zero. When the shift size is small, close to the origin, there will be some mix-up between shift and no shift case, while at large shift case, far from the origin, there is usually no mix-up with no shift case. The ARL at zero shift size is close to 373; that is, on average, a false alarm is issued every 373 data points. For the case of small shift 0.5 standardized shift (the system mistakenly classifies this as no shift for a large number of samples), nearly 150 data points are passed before the shift is discovered. A better performing system may reduce the number of false alarms while keeping the 0.5 standard shift at 150, or keeping the ARL at zero shift to 373 while reducing the ARL for 0.5 sigma to less than 150, or it can increase the ARL at zero shift and reduces the ARL at 0.5 sigma to less than 150.

Bottom Line: Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble.In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity.It also can be used to discover even small shifts in the mean as early as possible.

View Article: PubMed Central - PubMed

Affiliation: IE Department, The University of Jordan, Amman 11942, Jordan.

ABSTRACT
Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

No MeSH data available.