Deep learning-driven data curation and model interpretation for smart manufacturing
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …
Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion
Y Wang, J Zhao, C Yang, D Xu, J Ge - Measurement, 2022 - Elsevier
In the feature selection process, a subset of features is selected from the original set of
features based on the feature redundancy and importance. However, most of the existing …
features based on the feature redundancy and importance. However, most of the existing …
Outlier detection for monitoring data using stacked autoencoder
Monitoring data contain the important status information of the monitored object, and are the
basis for following data mining and analysis. However, the monitoring data usually suffer the …
basis for following data mining and analysis. However, the monitoring data usually suffer the …
A nonparametric adaptive EWMA control chart for monitoring mixed continuous and categorical data using self-starting strategy
L Xue, Q Wang, L An, Z He, S Feng, J Zhu - Computers & Industrial …, 2024 - Elsevier
In the context of big data-driven smart manufacturing, data is often characterized by high
dimensionality, numerous variables, and complex associations. As a result, mixed …
dimensionality, numerous variables, and complex associations. As a result, mixed …
PCA-based Hotelling's T2 chart with fast minimum covariance determinant (FMCD) estimator and kernel density estimation (KDE) for network intrusion detection
In this work, the combination between the Principal Component Analysis (PCA) and the
Hotelling's T 2 chart is proposed to solve problems caused by the many highly correlated …
Hotelling's T 2 chart is proposed to solve problems caused by the many highly correlated …
A nonparametric EWMA control chart for monitoring mixed continuous and count data
L Xue, Q Wang, Z He, P Qiu - Quality Technology & Quantitative …, 2024 - Taylor & Francis
Conventional statistical process control tools monitor either continuous or count data but
rarely both simultaneously. While process data are becoming increasingly complex, there …
rarely both simultaneously. While process data are becoming increasingly complex, there …
Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics
The products are commonly measured by two types of quality characteristics. The variable
characteristics measure the numerical scale. Meanwhile, the attribute characteristics …
characteristics measure the numerical scale. Meanwhile, the attribute characteristics …
Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables
W Rojas-Preciado, M Rojas-Campuzano… - Mathematics, 2023 - mdpi.com
Simple Summary The T2Qv control chart is presented as a multivariate statistical process
control technique that performs an analysis of qualitative data through Multiple …
control technique that performs an analysis of qualitative data through Multiple …
Multivariate control chart based on Kernel PCA for monitoring mixed variable and attribute quality characteristics
The need for a control chart that can visualize and recognize the symmetric or asymmetric
pattern of the monitoring process with more than one type of quality characteristic is a …
pattern of the monitoring process with more than one type of quality characteristic is a …
Performance of T2-based PCA mix control chart with KDE control limit for monitoring variable and attribute characteristics
In this work, the mixed multivariate T 2 control chart's detailed performance evaluation based
on PCA mix is explored. The control limit of the proposed control chart is calculated using …
on PCA mix is explored. The control limit of the proposed control chart is calculated using …