ComparisonIntermediate
According to the chapter, which machine learning algorithm achieved the highest classification accuracy for gearbox vibration data, and what accuracy was reported for gear 4 at 750 rpm in Table 7.6?
The chapter identifies support vector machine (SVM) as the top-performing algorithm for gearbox vibration data. In Table 7.6, the SVM accuracy reported for gear 4 at 750 rpm is 86.8%.
The results and conclusion state that SVM outperformed the other machine learning and deep learning algorithms on the vibration data. In Table 7.6, in the 750 rpm and gear 4 row, the SVM column reports 86.8%. The same row also lists the decision tree value as 99.5%, but the chapter's overall comparison across conditions concludes that SVM performs better.
Key points
- SVM is described as the most efficient and highest-accuracy classifier for this gearbox vibration task.
- Table 7.6 compares DT, SVM, ANN, and DNN variants on vibration data.
- For gear 4 at 750 rpm, the SVM value in Table 7.6 is 86.8%.
- In that same row, the decision tree is listed at 99.5%, but the overall chapter conclusion favors SVM.
Source:AI and Machine Learning for Mechanical and Electrical Engineering ...· The Implementation of Artificial Intelligence for Auto Gearbox Failure Detection· p. 112–124
AI and Machine Learning for Mechanical and Electrical Engineering ...
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First edition · CRC Press