Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Lin, Tzu-Yang

doi:10.1155/2019/5736104

Cited by 6 publications

(12 citation statements)

References 19 publications

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“…Dynamic weight-based data processing framework A data processing framework derived from image identification scheme 18 was proposed to dynamically select optimal PCA-based weights for data detection. Figure 1 presents a schematic of the weight-based processing framework.…”

Section: Proposed Methodsmentioning

confidence: 99%

Dynamic weight-based learning method for data detection in manufacturing

Lin

2014

Advances in Mechanical Engineering

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In this study, a dynamic weight-based method combined with principal component analysis (PCA) was developed for the first time for detecting measurement data in manufacturing. This weight-based learning technique can learn and train the measurement data sequence to isolate incorrect data sources for achieving high accuracy when detecting various types of data. Research has revealed that unsuitable image or data features might cause poor performance in industrial inspections. In contrast to the previous inspection methods, the weight-based learning method proposed in this study employs a dynamic learning algorithm for effectively and adaptively selecting optimal principle components to the support vector machine (SVM) algorithm and then establishes indicators. Finally, these PCA-based indicators act as substitutes for massive amounts of data in data processing and can be applied to timely detect data when the data contain redundant and incorrect inputs in a sequence. The experimental results indicate that the proposed method, which combines dynamic weight-based feature extraction with PCA, can provide useful indicators for detecting various types of manufacturing data and exhibited satisfactory performance in the data detection.

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Section: Proposed Methodsmentioning

confidence: 99%

Dynamic weight-based learning method for data detection in manufacturing

Lin

2014

Advances in Mechanical Engineering

Self Cite

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“…Examples of feature selection algorithms that have been widely used within a manufacturing system context include Principal Component Analysis [31], ReliefF [32] and Correlation Based Filter Selection [33]. These feature selection algorithms have their specific uses and scope, benefits and limitations and types of dataset they can handle.…”

Section: Feature Selection Modellingmentioning

confidence: 99%

A predictive noise correction methodology for manufacturing process datasets

Oleghe¹

2020

J Big Data

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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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“…Examples of feature selection algorithms that have been widely used within a manufacturing system context include Principal Component Analysis [26], ReliefF [27] and Correlation Based Filter Selection [28]. These feature selection algorithms have their speci c uses and scope, bene ts and limitations and types of dataset they can handle.…”

Section: Feature Selection Modellingmentioning

confidence: 99%

A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

Oleghe¹

2020

Preprint

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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.

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Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Cited by 6 publications

References 19 publications

Dynamic weight-based learning method for data detection in manufacturing

Dynamic weight-based learning method for data detection in manufacturing

A predictive noise correction methodology for manufacturing process datasets

A Predictive Noise Correction Methodology for Manufacturing Systems Datasets

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