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Gaussian mixture continuously adaptive regression for multimode processes soft sensing under time-varying virtual drift
Due to time-varying virtual drift in multimode processes, the performance of soft sensors will
degrade after online deployment. Traditional adaptive mechanisms have been developed to …
degrade after online deployment. Traditional adaptive mechanisms have been developed to …
A four-terminal-architecture cloud-edge-based digital twin system for thermal error control of key machining equipment in production lines
Production lines are important for the high-accuracy and efficient machining of parts. The
thermal error of key machining equipment in production lines has a significant effect on the …
thermal error of key machining equipment in production lines has a significant effect on the …
[HTML][HTML] A novel order analysis and stacked sparse auto-encoder feature learning method for milling tool wear condition monitoring
Milling is a main processing mode of the modern manufacturing industry, which seriously
affects the quality and precision of the machined workpiece. However, it is difficult to monitor …
affects the quality and precision of the machined workpiece. However, it is difficult to monitor …
An in-process tool wear assessment using Bayesian optimized machine learning algorithm
Cutting tool wear monitoring (TWM) plays a significant role because it guarantees the
machined surface integrity. Therefore, the present article proposed a TWM system using …
machined surface integrity. Therefore, the present article proposed a TWM system using …
Prediction of truck productivity at mine sites using tree-based ensemble models combined with Gaussian mixture modelling
In the past decade, machine learning (ML) algorithms have been widely applied to build
prediction models for various mining applications. However, no research has been reported …
prediction models for various mining applications. However, no research has been reported …
[PDF][PDF] Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites
The historical datasets at operating mine sites are usually large. Directly applying large
datasets to build prediction models may lead to inaccurate results. To overcome the real …
datasets to build prediction models may lead to inaccurate results. To overcome the real …
Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework
Controlled removal of material plays a significant role in subtractive machining which
shapes a job into the preferred size. The term 'controlled'implies the coordination of a cutting …
shapes a job into the preferred size. The term 'controlled'implies the coordination of a cutting …
Cutting force embedded manifold learning for condition monitoring of vertical machining center
J Wang, X Cheng, Y Gao, X Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Cutting condition monitoring is essential for managing the operation of machine tools in
manufacturing. The t-distributed stochastic neighbor embedding (t-SNE) method has been …
manufacturing. The t-distributed stochastic neighbor embedding (t-SNE) method has been …
[HTML][HTML] Semi-supervised machine condition monitoring by learning deep discriminative audio features
In this study, we aim to learn highly descriptive representations for a wide set of machinery
sounds and exploit this knowledge to perform condition monitoring of mechanical …
sounds and exploit this knowledge to perform condition monitoring of mechanical …
Asymmetric HMMs for online ball-bearing health assessments
The degradation of critical components inside large industrial assets, such as ball-bearings,
has a negative impact on production facilities, reducing the availability of assets due to an …
has a negative impact on production facilities, reducing the availability of assets due to an …