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Research on Integrated SVDD Rotating Machinery Vibration Fault Detection Method Based on Deep Autoencoder

机译：基于深度自动编码器的集成SVDD旋转机械振动故障检测方法研究

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摘要

There are some problems in vibration equipment of rotating machinery, such as complicated mechanism, confused field data and fewer fault samples. In order to improve the accuracy of fault detection, this paper proposes a rotating mechanical vibration fault detection method based on deep autoencoder and integrated SVDD(DAE-ISVDD). First, use the original normal sample data to train multiple different deep autoencoders, after that the trained autoencoders are used to extract features from the original data, then use the extracted features to train multiple basic SVDD weak classifiers, and finally, a strong classifier is obtained by means of weighted integration. Through the example verification, the resulting strong classifier can effectively improve the accuracy of the vibration fault detection of rotating machinery. Meanwhile, compared with other traditional methods, this method has good anti-noise performance and generalization ability, and can be well applied in practical engineering.
机译：旋转机械的振动设备存在机构复杂，现场数据混乱，故障样本少等问题。为了提高故障检测的准确性，提出了一种基于深度自动编码器和集成SVDD（DAE-ISVDD）的旋转机械振动故障检测方法。首先，使用原始的正常样本数据来训练多个不同的深度自动编码器，然后使用经过训练的自动编码器从原始数据中提取特征，然后使用提取的特征来训练多个基本SVDD弱分类器，最后，一个强分类器是通过加权积分获得。通过实例验证，所得到的强分类器可以有效提高旋转机械振动故障检测的准确性。同时，与其他传统方法相比，该方法具有良好的抗噪性能和泛化能力，可以很好地应用于实际工程中。

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