In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.

中文翻译：

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机器学习在中等高斯支持向量机中在电机轴承故障诊断中的应用

近年来，人工智能技术在故障预测和健康管理（PHM）方面得到了广泛的应用。机器学习算法广泛应用于旋转机械的状态监测，通过数据采集和监测系统可以获得正常和故障数据。在对数据进行分析并建立模型后，系统可以自动从输入数据中学习特征，从而预测维修和诊断设备的故障，这对电机维修很重要。本研究提出了一种用于机器学习应用的中高斯支持向量机（SVM）方法，并根据经验提取现场采集的振动信号的特征来构建特征空间。使用不同的方法对特征进行聚类和分类以对运动健康进行分类。分析了细、中、粗等不同高斯核函数对SVM算法性能的影响。实验数据通过凯斯西储大学电机轴承数据中心发布的数据集验证了各种模型的性能。由于电机在实际应用环境中经常存在噪声干扰，为了验证研究方法在噪声环境下的性能，在原始振动数据中加入了模拟高斯白噪声。结果总结了最近使用不同机器学习算法进行电机故障检测和诊断得出的相关电机数据集的分类结果。结果表明，中等高斯支持向量机方法提高了电机轴承故障估计、检测和识别在可变裂纹尺寸和负载条件下的可靠性和准确性。本文还详细讨论了机器学习算法的预测分析能力，可以作为未来电动汽车电机预测性维护分析的参考。