How SVM is better than knn decisiontree, random forest for bearing fault analysis using vibration signal?
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Support Vector Machines (SVM) combined with Fast Fourier Transform (FFT) has been shown to outperform other shallow classifiers such as K-Nearest Neighbors (KNN), Decision Tree, and Random Forest for bearing fault analysis using vibration signal . The combined SVM-FFT approach achieves higher accuracy (>99%) in diagnosing broken bearings . Another study also supports the superiority of SVM over Random Forest and Probabilistic Neural Network (PNN) for bearing fault diagnosis . Additionally, a fused Bidirectional Long and Short Term Memory (BiLSTM) and SVM recurrent neural network deep learning algorithm has been proposed for fast fault diagnosis of mechanical bearing vibration. This algorithm demonstrates higher diagnostic accuracy and faster diagnosis speed compared to traditional methods .
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| Papers (5) | Insight |
|---|---|
| The paper does not compare SVM with KNN, decision tree, or random forest for bearing fault analysis using vibration signal. | |
| The provided paper does not mention SVM, KNN, decision tree, or random forest for bearing fault analysis using vibration signal. | |
| The provided paper does not mention SVM, KNN, decision tree, or random forest for bearing fault analysis using vibration signal. | |
08 Dec 2022 | The paper states that the combined SVM-FFT approach outperforms decision tree, random forest, and naive Bayes classifiers for diagnosing broken bearings using vibration signals. However, it does not specifically mention KNN. |
| The paper does not mention KNN, Decision Tree, or Random Forest. It only compares SVM-FFT with SVM-Statistic and states that SVM-FFT outperforms SVM-Statistic in diagnosing broken bearings with higher accuracy. |
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