scholarly journals PEMODELAN KERANGKA ADAPTIVE THRESHOLD UNTUK MEMONITOR PRODUKSI MINYAK SAWIT NASIONAL BERBASIS STATISTICAL PROCESS CONTROL DAN ARTIFICIAL NEURAL NETWORK-BACKPROPAGATION

2016 ◽  
Vol 11 (2) ◽  
pp. 113-122
Author(s):  
Wahyu Widji Pamungkas ◽  
Syamsul Maarif ◽  
Tun Tedja Irawadi ◽  
Yandra Arkeman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Control ◽  
Statistical Process Control ◽  
Palm Oil ◽  
Historical Data ◽  
Adaptive Threshold ◽  
The Other ◽  
Production Volume ◽  
Statistical Process

Indonesia is the largest exporter of palm oil in the world, as the largest producer Indonesia still havemany problems. The problem caused by incomparable between the growth of upstream and downstreampalm oil industries. This impact to low added value of palm oil, then Indonesia exports palm oil in crudeform. On the other hand, On the other hand , orientation export of this commodity is also prone of barrier,because Indonesia was not the price setter of this commodity in the international market. Therefore it isimportant to monitor and predict the development of national palm oil production volume in order to takegood anticipation. This research develop a framework model adaptive threshold to monitor the growing ofnational palm oil production volume with techniques of statistical process control (SPC) and back propagationartificial neural network (ANN - BP) methods. Historical data production volume period from 1967 to 2015was used as a base of the behavior as data to determine the threshold and prediction volume for nextperiods. The formation of the threshold value was based on the behavior of the historical data, which areoriented by the epicenter of the average value in the last two periods .Through mapping of data historicalperiod values, existing and forecast values with adaptive threshold can show tolerant level for the threshold.Furthermore, based on the analysis, it is known that the prediction of 2016 to 2018 period, there will behappen the dynamics production volume of national palm oil within tolerance threshold. The values of thesepredictions generated from the simulation model predictions of ANN-BP with the level very good of validationmodel, demonstrated the level of squared errors is very small1 in the MSE = 0.00021136 with a degree ofoutput correlation and the target is very strong2 with R Validation is 99.98 percent.Keywords: adaptive threshold, statistical process control, artificial neural network, national palm oilproduction.

Paraconsistent Artificial Neural Network for Structuring Statistical Process Control in Electrical Engineering

2016 ◽  
pp. 77-102
Author(s):  
João Inácio da Silva Filho ◽  
Clovis Misseno da Cruz ◽  
Alexandre Rocco ◽  
Dorotéa Vilanova Garcia ◽  
Luís Fernando P. Ferrara ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Control ◽  
Statistical Process Control ◽  
Electrical Engineering ◽  
Statistical Process ◽  
Artificial Neural

Using Statistical Process Control Charts to Study Stuttering Frequency Variability During a Single Day

2013 ◽  
Vol 56 (6) ◽  
pp. 1789-1799 ◽  
Author(s):  
Hamid Karimi ◽  
Sue O’Brian ◽  
Mark Onslow ◽  
Mark Jones ◽  
Ross Menzies ◽  
...  
Keyword(s):  
Process Control ◽  
Control Systems ◽  
Statistical Process Control ◽  
Control Charts ◽  
Communication Satisfaction ◽  
The Other ◽  
Marked Variation ◽  
Statistical Process ◽  
Process Control Charts ◽  
Statistical Process Control Charts

Purpose Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech during different speaking activities over a 12-hr day. Results for each participant were plotted on control charts. Results All participants showed marked variation around mean stuttering frequency. However, there was no pattern of that variation consistent across the 10 participants. According to control charts, the %SS scores of half the participants were indicative of unpredictable, out-of-control systems, and the %SS scores of the other half of the participants were not. Self-rated stuttering severity and communication satisfaction correlated significantly and intuitively with the number of times participants exceeded their upper control chart limits. Conclusions Control charts are a useful method to study how %SS scores might be applied to the study of stuttering variability during research and clinical practice. However, the method presents some practical problems, and the authors discuss how those problems can be solved. Solving those problems would enable researchers and clinicians to better plan, conduct, and evaluate stuttering treatments.

Determining Statistical Process Control Baseline Periods in Long Historical Data Streams

2010 ◽  
Vol 42 (1) ◽  
pp. 21-35 ◽  
Author(s):  
Hang Zhang ◽  
Susan L. Albin ◽  
Steven R. Wagner ◽  
Daniel A. Nolet ◽  
Suhail Gupta
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Data Streams ◽  
Historical Data ◽  
Statistical Process

scholarly journals Comparison of grouping algorithms to increase the sample size for statistical process control

2020 ◽  
Vol 2 (5) ◽  
Author(s):  
Jonathan Simon Greipel ◽  
Gina Nottenkämper ◽  
Robert Heinrich Schmitt
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Small Sample ◽  
The Other ◽  
Sample Sizes ◽  
Performance Indices ◽  
Statistical Process ◽  
Production Processes ◽  
Volume Production ◽  
Low Volume

Abstract In this study, we present and compare four grouping algorithms to combine samples from low volume production processes. This increases their sample sizes and enables the application of Statistical Process Control (SPC) to low volume production processes. To develop the grouping algorithms, we define different grouping criteria and a general grouping process. To identify which algorithm is optimal, we deduct following requirements on the algorithms from real production datasets: their ability to handle different amount of characteristics and sample sizes within each characteristic as well as being able to separate characteristics possessing distributions with different spreads and locations. To check the fulfillment of these requirements, we define two performance indices and conduct a full-factorial Design of Experiments. We achieve the performance indices for each algorithm by using simulations with artificial data incorporating the aforementioned requirements. One index rates the achieved group sizes and the other one the compactness within groups and the separation between groups. To validate the applicability of grouping algorithms within SPC, we apply real production data to the grouping algorithms and control charts. The result of this analysis shows that the grouping algorithm based on cluster analysis and splitting exceeds the other algorithms. In conclusion, the grouping algorithms enable the application of SPC to small sample sizes. This provides companies, which produce in low volumes, with new means of reducing scrap, generating process knowledge and increasing quality.

scholarly journals End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1484
Author(s):  
Chuen-Sheng Cheng ◽  
Ying Ho ◽  
Tzu-Cheng Chiu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Process Control ◽  
Convolutional Neural Network ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process ◽  
Random Patterns ◽  
Control Chart Pattern

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.

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