AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor...

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Bibliographic Details
Main Authors:	Syed Safdar Hussain, Syed Sajjad Haider Zaidi
Format:	Article
Language:	English
Published:	MDPI AG 2024-04-01
Series:	Applied Sciences
Subjects:	DC motor diagnosing AdaBoost weak classifiers fault severity conditions remaining useful life
Online Access:	https://www.mdpi.com/2076-3417/14/7/3105

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