Perturbation theory for cross data matrix-based PCA

Wang, Shao-Hsuan; Huang, Su‐Yun

doi:10.1016/j.jmva.2022.104960

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to screen for volatile compounds that can help distinguish different beer samples, PCA, a technique used to explore high-dimensional data structures by reducing the dimension of the data to identify more understandable features and accelerating the processing of valuable sample information, could be used to screen feature components effectively to distinguish different samples ( Wang & Huang, 2022 ) and was used to analyze the differences in major flavor compounds in different beer samples. As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

Gao,

Li,

Wang

et al. 2024

Food Chemistry: X

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

Gao,

Li,

Wang

et al. 2024

Food Chemistry: X

View full text Add to dashboard Cite

“…Principal component analysis (PCA) is a typical method for feature extraction and data analysis, and an effective tool for dimensionality reduction analysis [37]. Wang et al [38] obtained the finite sample approximate result of CDM-based PCA through matrix perturbation and obtained the final estimate of CDM-based PCA. Zhou et al [39] improved the diagnostic accuracy by combining PCA and contribution analysis for fault isolation.…”

Section: Introductionmentioning

confidence: 99%

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

Cheng,

Cao,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

show abstract

“…Wang et al [34] obtained the finite sample approximate result of CDM-based PCA through matrix perturbation and the final estimate of CDM-based PCA. Zhou et al [35] improved the diagnostic accuracy by combining PCA and contribution analysis for fault isolation.…”

Section: Introductionmentioning

confidence: 99%

An improved self-organizing mapping neural network and its application in fault diagnosis of CNC machine tool servo drive system

Cheng¹,

Cao²,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

The computer numerically controlled (CNC) system is the main functional component of the CNC machine tool control system, whose essential part is the servo-drive system. The failure cause of the servo-drive system can be challenging to diagnose due to the complex working environment. Therefore, effectively detecting servo-drive system faults ensures the regular operation of CNC machine tools. In this paper, an improved self-organizing mapped neural network method is proposed and applied to fault diagnosis of the servo-drive system. The MDC technology can identify hidden fault features in data and diagnose fault types based on various data collected by the MDC system and working parameter range indicators. The method's core is the self-organizing mapped neural network that employs the competitive learning mechanism of unsupervised learning to perform cluster analysis on data with different characteristics, find the winning neurons, and diagnose the fault data. In addition, feature standardization and principal component analysis are introduced to preprocess the input data set, which can balance the influence of different feature scales, enhance the fault data features, and reduce data dimension. The rationality of this technique in practical application is validated via a series of fault data sets tests. Lastly, the advantages of the proposed technique are verified by comparison with other standard methods.

show abstract

Perturbation theory for cross data matrix-based PCA

Cited by 7 publications

References 14 publications

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

An improved self-organizing mapping neural network and its application in fault diagnosis of CNC machine tool servo drive system

Contact Info

Product

Resources

About