Characterizing Strip Snap in Cold Rolling Process Using Advanced Data Analytics

Chen, Zheyuan; Liu, Ying; Valera‐Medina, Agustin; Robinson, Fiona

doi:10.1016/j.procir.2019.03.078

“…Data modellers have to choose whether to proceed with the full set or a reduced set, depending on the prediction accuracy of using the reduced set in comparison to using the full set. In a very large dataset with thousands of features, it is good practice to exclude non-informative and irrelevant features first [39] as this can enhance the feature selection process. Other pre-processing tasks that enhance feature selection modelling include using clean instances of the dataset [40] and data type transformation, such as normalization, discretization and nominalization, as would normally be fulfilled prior to classification, clustering and association modelling, for example.…”

Section: Feature Selection Modellingmentioning

confidence: 99%

A predictive noise correction methodology for manufacturing process datasets

Oleghe¹

2020

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In manufacturing processes, datasets intended for data driven decisions are majorly generated from time-sequenced sensor readings. Industrial sensor systems are prone to transmit inaccurate readings, which result in noisy datasets. Noisy datasets inhibit machine learning and knowledge discovery. Using a multi-stage, multi-output process dataset as an experimental case, this article reports a methodology for replacing erroneous sensor values with their predicted likely values. In the methodology, invalid values specified by process owners are first converted to missing values. Then, ReliefF algorithm is used to select the most relevant features to progress for prediction modelling, and also to boost the performance of the prediction model. A Random Forest classifier model is built to predict replacement values for the missing values. Finally, predicted values are inserted into the dataset to fill in the missing entries. With many attributes having a significant number of erroneous values, the invalid values replacement is done one attribute at a time. To do this systematically, the process flow direction and stages in the manufacturing process are exploited to partition the dataset into subsets for model building. The results indicate that the methodology is able to replace erroneous values with likely true values, to a very high degree of accuracy. There is a paucity of this type of methodology for dealing with invalid entries in process datasets. The methodology is useful for both missing and invalid value correction in process datasets. In the future, the plan is to inject the prediction models into streaming data to simultaneously enable erroneous value correction and predictive process monitoring in real-time.

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“…Data modellers have to choose whether to proceed with the full set or a reduced set, depending on the prediction accuracy of using the reduced set in comparison to using the full set. In a very large dataset with thousands of features, it is good practice to exclude non-informative and irrelevant features rst [34] as this can enhance the feature selection process. Other pre-processing tasks that enhance feature selection modelling include using clean instances of the dataset [35] and data type transformation, such as normalization, discretization and nominalization, as would normally be ful lled prior to classi cation, clustering and association modelling, for example.…”

Section: Feature Selection Modellingmentioning

confidence: 99%

A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

Oleghe¹

2020

Preprint

0

View full text Add to dashboard Cite

In manufacturing systems, datasets intended for data driven decisions are majorly generated from time-sequenced sensor readings. Industrial sensors are prone to transmit inaccurate readings due to various reasons, which results in noisy datasets. Noisy datasets inhibit machine learning and knowledge discovery. This article reports a methodology that was developed to rectify a very noisy dataset of a multi-stage continuous manufacturing process, with multi-feature output. In the methodology, erroneous values are first swapped with missing values. Then, feature reduction modelling is used as a precursor of prediction learning to improve machine learning performance. Afterwards, a classifier model is built to predict replacement values for the missing values. Finally, predicted values are inputted to replace missing values in the dataset. With many attributes having erroneous values, the values replacement is done one attribute at a time. The flow direction and stages in the manufacturing process are used in partitioning the dataset to improve learning performance. With the methodology, important system relationships are identified and the dataset is learnt for predictive process monitoring. There is a paucity of this type of methodology for dealing with noisy datasets in manufacturing systems. In the future, the plan is to inject the prediction model into streaming data to enable real-time erroneous value correction and real-time predictive process monitoring at the same time.

show abstract

“…Data modellers have to choose whether to proceed with the full set or a reduced set, depending on the prediction accuracy of using the reduced set in comparison to using the full set. In a very large dataset with thousands of features, it is good practice to exclude non-informative and irrelevant features rst [40] as this can enhance the feature selection process. Other pre-processing tasks that enhance feature selection modelling include using clean instances of the dataset [41] and data type transformation, such as normalization, discretization and nominalization, as would normally be ful lled prior to classi cation, clustering and association modelling, for example.…”

Section: Feature Selection Modellingmentioning

confidence: 99%

A predictive noise correction methodology for manufacturing process datasets

Oleghe¹

2020

Preprint

0

View full text Add to dashboard Cite

In manufacturing processes, datasets intended for data driven decisions are majorly generated from time-sequenced sensor readings. Industrial sensor systems are prone to transmit inaccurate readings, which result in noisy datasets. Noisy datasets inhibit machine learning and knowledge discovery. Using a multi-stage, multi-output process dataset as an experimental case, this article reports a methodology for replacing erroneous sensor values with their predicted likely values. In the methodology, invalid values specified by process owners are first converted to missing values. Then, ReliefF algorithm is used to select the most relevant features to progress for prediction modelling, and also to boost the performance of the prediction model. A Random Forest classifier model is built to predict replacement values for the missing values. Finally, predicted values are inserted into the dataset to fill in the missing entries. With many attributes having a significant number of erroneous values, the invalid values replacement is done one attribute at a time. To do this systematically, the process flow direction and stages in the manufacturing process are exploited to partition the dataset into subsets for model building. The results indicate that the methodology is able to replace erroneous values with likely true values, to a very high degree of accuracy. There is a paucity of this type of methodology for dealing with invalid entries in process datasets. The methodology is useful for both missing and invalid value correction in process datasets. In the future, the plan is to inject the prediction models into streaming data to simultaneously enable erroneous value correction and predictive process monitoring in real-time.

show abstract

Characterizing Strip Snap in Cold Rolling Process Using Advanced Data Analytics

Cited by 12 publications

References 27 publications

A predictive noise correction methodology for manufacturing process datasets

A predictive noise correction methodology for manufacturing process datasets

A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

A predictive noise correction methodology for manufacturing process datasets

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