Multistage Quality Control Using Machine Learning in the Automotive Industry

Peres, Ricardo Silva; Barata, José; Leitão, Paulo; Garcia, Gisela

doi:10.1109/access.2019.2923405

“…Real-life datasets are often noisy [5,6], to varying degrees such as low, moderate, high and extreme [7]. Table 1 can be used to explain these degrees in a given m x n dataset matrix, where m is the number of attributes or columns and n the number of instances or rows.…”

Section: Dataset De-noising and Erroneous Values Correctionmentioning

confidence: 99%

“…In data modelling with classification learning, it is common to use a boosting method such as an ensemble of multiple classifiers [8] in order to improve prediction accuracy. Random Forest classifier is an ensemble of tree-based classification models, and is known to be more accurate than J48 and Decision Tree [5]. Feature reduction has also been used to boost the performance of classifier models.…”

Section: Classification 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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“…Real-life datasets are often noisy [6], to varying degrees such as low, moderate, high and extreme [7]. There are as many as four commonly occurring types of patterns for erroneous values in large dataset [8].…”

Section: Related Workmentioning

confidence: 99%

“…Tree-based classi ers can handle small to medium sized datasets very well [43,44] In data modelling with classi cation learning, it is common to use a boosting method such as an ensemble of multiple classi ers [9] in order to improve prediction accuracy. Random Forest classi er is an ensemble of tree-based classi cation models, and is known to be more accurate than J48 and Decision Tree [6]. Feature reduction has also been used to boost the performance of classi er models.…”

Section: Classi Cation Modellingmentioning

confidence: 99%

A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

Oleghe¹

2020

Preprint

0

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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

“…In data modelling with classi cation learning, it is common to use a boosting method such as an ensemble of multiple classi ers [9] in order to improve prediction accuracy. Random Forest classi er is an ensemble of tree-based classi cation models, and is known to be more accurate than J48 and Decision Tree [5]. Feature reduction has also been used to boost the performance of classi er models.…”

Section: Classi Cation 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

Multistage Quality Control Using Machine Learning in the Automotive Industry

Cited by 100 publications

References 19 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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