A Fault Detection for a Correlated Process with the Use of SPC/EPC/NN Scheme

Shao, Yuehjen E.

doi:10.1109/icicic.2009.13

Cited by 12 publications

(21 citation statements)

References 14 publications

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“…Since more than 75% of ANN applications use the back-propagation neural network (BPNN), this study employs the BPNN in developing the ANN forecasting model [25]- [28]. In addition, since there is only one variable (i.e., y t , or IECES) available, this study uses two designs for ANN modeling.…”

Section: Ann Modeling Resultsmentioning

confidence: 99%

“…The hidden nodes were chosen as 22, 23, 24, 25 and 26 for the second design. Since the learning rate of 0.01 is a very effective setting [22], [28], this study sets the values of learning setting as 0.01 for ANN modeling. After performing the ANN modeling, the first design obtained that the {2-6-1} and {2-2-1} structures provided the best results and a minimum testing MAPE for IE and CE sales, respectively.…”

Section:  Nmentioning

confidence: 99%

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Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Shao¹,

Tsai²

2018

IJEEEE

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Electricity is one of the most important sources of energy on earth. Today, electricity has become a part of our life. Electricity is the key component to modern technology and without it most of the products that we use simply could not work. Without doubt, the economic growth for almost every country in the world is affected by electricity rates. Therefore, the prediction of electricity sales is very important for Taiwanese economy. This study employs the autoregressive integrated moving average (ARIMA), artificial neural networks (ANN) and the integrated ARIMA-ANN approaches for predicting the industrial electricity and commercial electricity sales (IECES) in Taiwan. The forecasting accuracy measure is based on the mean absolute percentage error. The real dataset, from the years 2006 to 2016, for IECES in Taiwan are collected and analyzed. The prediction results show that the ARIMA-ANN model has the most satisfactory forecasting accuracy for predictions of IECES in Taiwan.

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Section: Ann Modeling Resultsmentioning

confidence: 99%

Section:  Nmentioning

confidence: 99%

Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Shao¹,

Tsai²

2018

IJEEEE

Self Cite

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“…These information are often used to adjust the machine parameters after the event. SPC control charts are important tools to diagnose abnormalities of process [5,16]. Using these charts, engineers can understand the quality of the wafer.…”

Section: Quality Control In Semiconductor Manufacturingmentioning

confidence: 99%

“…When the parameters deviate from the original value, and may be beyond the range of the set interval, this needs to apply the Run-to-Run adjustments to modify the parameters directly and continuously collecting the running parameters of the machine and constant feedbacks [1]. Based on the previous activities of quality control, engineers can adjust the machine parameters to ensure that the production are within normal operations [15][16].…”

Section: Quality Control In Semiconductor Manufacturingmentioning

confidence: 99%

Do peak/slack seasons influence semiconductor machine outliers? A back propagation neural network analysis

2017

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Abstract. The semiconductor industry is often affected by the economy impact, which also influences the production schedule planning. The back propagation neural network model has the advantages of great precision and effectiveness. This research uses Novellus Vector Machine and its Remote Process Controller (RPC) function to collect the data. This study detects the gas transmission pressure of chamber. We uses fault detection and classification (FDC) to analyze the model. FDC can detect the deviations of the machine parameters when the parameters deviate from the original value and exceed the range of the specification. This study adopts back propagation neural network model and gray relational analysis as tools to analyze the data and detect the semiconductor machine outliers. The findings indicate that peak seasons have less outliers than slack seasons.

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“…Vibrations that result from these dynamic forces affect the desired precision. In rotational actuators, such as the air bearing spindle of ultra-precision machine tools, the dynamic forces produced during rotary movement [11] reflect these uncontrolled dynamics. The increase in dynamic forces, due to the dynamic mass imbalance of the spindle, generates an eccentricity on its rotational axis with its corresponding vibration.…”

Section: Ultra-precision Rotation Devices In Manufacturing Processesmentioning

confidence: 99%

Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

Toro¹,

Haber²

2017

Preprint

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ABSTRACT. Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

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A Fault Detection for a Correlated Process with the Use of SPC/EPC/NN Scheme

Cited by 12 publications

References 14 publications

Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Do peak/slack seasons influence semiconductor machine outliers? A back propagation neural network analysis

Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

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