Feature contributions to each PCA component.

<div><p>This study explores the design of an effective fault classification algorithm for 3 phase induction motor, an integral unit in many industrial systems. It is found that traditional fault detection methods and deep learning approaches are both effective; however, current technique...

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Bibliographic Details
Main Author:	Zawar Ahmed Khan (22574556) (author)
Other Authors:	Muhammad Amir Raza (22391989) (author), Muhammad I. Masud (22574559) (author), Touqeer Ahmed Jumani (12536455) (author), Muhammad Faheem (1767637) (author), Mohammed Aman (19665349) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified principal component analysis higher computational cost frequency domain information fast fourier transform extreme gradient boosting deep learning approaches many industrial systems knn performs well 7 &# 8201 dt ), k knn ), many real &# 8217 xlink "> world settings work serves various loads thus making synthetic minority study explores smote ). sampling technique rotor faults results shows random forest nearest neighbors motor specifications mm bearing missing values intelligent framework integral unit generally known future work feature set feature selection fault types dimensionality reduction data preprocessing current variables current signals computationally exhaustive best possible additional feature
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Feature contributions to each PCA component.

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