<br>融合多核 SVM 互相关的轴承同时故障检测,IEEE Sensors Journal

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融合多核 SVM 互相关的轴承同时故障检测
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2023-05-18 , DOI: 10.1109/jsen.2023.3276022
Anadi Biswas ₁ , Susanta Ray ₁ , Debangshu Dey ₁ , Sugata Munshi ₁

Affiliation

使用时域分析检测同步轴承故障以进行轴承状态监测 (CM) 是相当具有挑战性和开放性的领域，特别是在嘈杂的环境中。这项工作提出了一种在单点局部轴承故障和多点复合故障（MPCF）共存的情况下使用振动信号（VS）同时检测轴承故障的新方案。 415V、3kW、三相鼠笼感应电机 (SCIM) 的轴承用于数据收集，而负载布置是使用与负载箱连接的 110V、4kW 直流发电机完成的并耦合到电机。引入了基于互相关（CC）的时域特征提取方法。邻域成分分析（NCA）技术已应用于基于 CC 的特征，以降低所提出模型的复杂性。此外，选定的特征已被输入到多核支持向量机（MKSVM）中，以对同时发生的轴承故障进行分类。该方法还针对受高斯白噪声污染的信号进行了测试，以验证工业环境中的可靠性。结果发现，仅用五个特征，所提出的模型就可以在原始信号（RS）和满载（FL）的信噪比（SNR）为 20-50 dB 的噪声环境下产生 100% 的分类性能指标。）和空载（NL）条件。相比之下，在 10 dB SNR 值下，性能略有下降，但该方法仍然实现了 99% 以上的总体分类性能指标。此外，与早期使用公开数据库进行局部轴承故障识别的研究相比，该方法具有增强的性能。

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Detection of Simultaneous Bearing Faults Fusing Cross Correlation With Multikernel SVM

Detection of simultaneous bearing faults for condition monitoring (CM) of bearings using time-domain analysis is quite challenging and open area, particularly in noisy environment. This work presents a new scheme for simultaneous bearing fault detection using vibration signal (VS), in cases where single-point localized bearing fault and multiple-point compound fault (MPCF) coexist. Bearings of a 415-V, 3-kW, three-phase squirrel cage induction motor (SCIM) have been used for data collection, while the loading arrangement is done using a 110-V, 4-kW dc generator connected with a load box and coupled to the motor. A cross correlation (CC)-based time-domain feature extraction approach has been introduced. The neighborhood component analysis (NCA) technique has been applied to the CC-based features to reduce the complexity of the proposed model. Furthermore, the selected features have been fed into a multikernel support vector machine (MKSVM) to classify simultaneous bearing faults. This method has also been tested on signals contaminated with white Gaussian noise to verify reliability in the industrial environment. It is found that with only five features, the proposed model yields 100% classification performance metrics for raw signal (RS) and under noisy environments with a signal-to-noise ratio (SNR) of 20–50 dB for both full load (FL) and no-load (NL) conditions. In contrast, at 10-dB SNR value, performance decreases slightly, still an overall classification performance metric of more than 99% is achieved by this method. Furthermore, this method has enhanced performance when compared to earlier studies with publicly available databases for localized bearing failure identification.

更新日期：2023-05-18

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