Novel Mahalanobis Distance and Variable Nearest Neighbors to Construct Weight Matrix based LPP: Application of Fault Diagnosis

Fault diagnosis techniques based on data-driven algorithms go mainstream gradually in industrial processes. Unfortunately, traditional data-driven algorithms cannot deal with massive high-dimensional and nonlinear strong correlation data. Based on this problem, Novel Mahalanobis Distance and Variabl...

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Veröffentlicht in:	Chinese Automation Congress (Online) S. 2263 - 2267
Hauptverfasser:	Zhu, Qun-Xiong, Qing, Hao-Yang, Zhang, Ning, Xu, Yuan, He, Yan-Lin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 25.11.2022
Schlagworte:	Automation Correlation Euclidean distance Fault diagnosis Interference Locality Preserving Projections Manifolds Nearest Neighbors Shape Tennessee Eastman Process
ISSN:	2688-0938
Online-Zugang:	Volltext
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