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Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

机译：基于支持向量机的轴承故障检测技术比较

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This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.
机译：本文提出了一种结合改组蛙跳算法（SFLA）和支持向量机（SVM）方法的方法，以识别齿轮箱中滚动轴承的故障类型。通过小波阈值去噪处理振动信号，提高了故障诊断识别的准确性。 SFLA的全局优化和高计算效率被应用于SVM模型。仿真结果表明，SFLA-SVM算法在故障诊断中是有效的。与支持向量机和粒子群优化支持向量机（PSO-SVM）算法相比，证明了SFLA-SVM算法具有更好的全局优化，更高的准确性和更好的诊断可靠性。通过集成小波阈值去噪方法，可以进一步提高其精度。

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