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- W4386035947 abstract "The selection of the optimum machine learning technique is a crucial step to detect faults efficiently in the predictive maintenance (PdM) area. Because the performance of the machine learning algorithm changes with respect to a data set, which has different characteristics, including feature number, data size and nonlinearity. The paper considers the problem of detecting faults observed in an autonomous electric drive without using any sensor information. More importantly, we aim to show the opportunities and explore the limitations of machine learning techniques in fault detection. Accordingly, the advantages and disadvantages of different types of machine learning methods including logistic regression, support vector machine, decision tree, navie Bayes, gradient boosting etc. for condition monitoring are discussed with an emphasis given to an autonomous electric drive train. Experimental comparison of machine learning algorithms suggests that the boosting methods yield promising performance in fault classification. The results are supported by statistical analysis." @default.
- W4386035947 created "2023-08-22" @default.
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- W4386035947 date "2023-04-13" @default.
- W4386035947 modified "2023-09-25" @default.
- W4386035947 title "Condition Monitoring of an Autonomous Electric Drive Train by Using Machine Learning Methods" @default.
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- W4386035947 doi "https://doi.org/10.1145/3594441.3594478" @default.
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