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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.


Mapping decision criteria for failure against normal and faulty process signals.
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Related In: Results  -  Collection


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fig1: Mapping decision criteria for failure against normal and faulty process signals.

Mentions: Pattern recognition in control charts is extremely important for increasing productivity and for better customer satisfaction. Manufacturing process is of random nature; however certain aspects are not random such as broken heater, worn bearings, unacceptable raw materials, worn coils, higher than needed temperature, or other process parameters. These are called assignable causes. Assignable causes (real process uncontrollable changes) can affect the process quality and must be discovered as early as possible while unassignable random causes (normal process variations) must be ignored. In case the process is allowed to continue working under assignable causes effect, quality deteriorates and more defects or defectives are produced. This case is called type II error. If the process is stopped when the process is still under normal variation (this is a false alarm) then the productivity is reduced. This is called type I error. Both errors are current in practice. Successful process control is a technique that minimizes both errors. There is usually a compromise between these two errors; that is, if we try to reduce type II error, that is, to make sure that more nonnormal causes are discovered, then more normal variations are mistaken as nonnormal and type I error is increased. If we try to reduce type I error, that is, to make sure that less normal variations are mistakenly classified as assignable, then more assignable causes are undiscovered and type II error is increased. Early discovery of faults is a subject of a large sum of current research. This is usually combined with online monitoring when this is related to manufacturing processes. Some examples of this failure monitoring are induction motor failure discovery using current signal [1], bearing fault detection [2], metal processing sensor fault diagnostics [3], and so forth. In all, the decision should be made of whether the process is subject to real change (assignable) or whether the process is still under normal causes. Figure 1 maps the generated patterns from normal and faulty processes against the decision criterion for failure. Area B shows patterns that can be generated by both faulty and normal processes and represents a challenge for the researchers. Areas A and E are successfully classified as faulty (A) when the process is faulty and not faulty (E) when it is not faulty. Areas D and C are the main decision mistakes leading to type II error (D) whereby the process is classified as normal while the process is actually faulty and type I error whereby the process is classified as faulty while it is actually normal.


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

Barghash M - Comput Intell Neurosci (2015)

Mapping decision criteria for failure against normal and faulty process signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Mapping decision criteria for failure against normal and faulty process signals.
Mentions: Pattern recognition in control charts is extremely important for increasing productivity and for better customer satisfaction. Manufacturing process is of random nature; however certain aspects are not random such as broken heater, worn bearings, unacceptable raw materials, worn coils, higher than needed temperature, or other process parameters. These are called assignable causes. Assignable causes (real process uncontrollable changes) can affect the process quality and must be discovered as early as possible while unassignable random causes (normal process variations) must be ignored. In case the process is allowed to continue working under assignable causes effect, quality deteriorates and more defects or defectives are produced. This case is called type II error. If the process is stopped when the process is still under normal variation (this is a false alarm) then the productivity is reduced. This is called type I error. Both errors are current in practice. Successful process control is a technique that minimizes both errors. There is usually a compromise between these two errors; that is, if we try to reduce type II error, that is, to make sure that more nonnormal causes are discovered, then more normal variations are mistaken as nonnormal and type I error is increased. If we try to reduce type I error, that is, to make sure that less normal variations are mistakenly classified as assignable, then more assignable causes are undiscovered and type II error is increased. Early discovery of faults is a subject of a large sum of current research. This is usually combined with online monitoring when this is related to manufacturing processes. Some examples of this failure monitoring are induction motor failure discovery using current signal [1], bearing fault detection [2], metal processing sensor fault diagnostics [3], and so forth. In all, the decision should be made of whether the process is subject to real change (assignable) or whether the process is still under normal causes. Figure 1 maps the generated patterns from normal and faulty processes against the decision criterion for failure. Area B shows patterns that can be generated by both faulty and normal processes and represents a challenge for the researchers. Areas A and E are successfully classified as faulty (A) when the process is faulty and not faulty (E) when it is not faulty. Areas D and C are the main decision mistakes leading to type II error (D) whereby the process is classified as normal while the process is actually faulty and type I error whereby the process is classified as faulty while it is actually normal.

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.