Apr 30, 2019 · Factor analysis is prominently being used in fault diagnosis of machines to identify the significant factors and to study the root cause of a specific machine ...
Factor analysis has been widely used in classification problems for identifying specific factors that are significant to particular classes.
Feb 28, 2019 · Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems.
A real case of an industrial rotating machine was considered where vibration and ambient temperature data was collected for monitoring the health of the ...
In this study, a framework for diagnosing steady state faults with random forests is proposed and demonstrated with a simple nonlinear system.
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Factor analysis has been widely used in classification problems for identifying specific factors that are significant to particular classes.
The proposed Random Forest models with SMOTE perform satisfactorily in diagnosing faults for the evaluation dataset, with a total accuracy of 96.2% for DPM1 and ...
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Factor analysis has been widely used in classification problems for identifying specific factors that are significant to particular classes.
Jan 8, 2022 · In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest ...
Apr 11, 2022 · This paper proposes a random forest and modified independent component analysis (RF-MICA) to detect the occurrence of PV faults.