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A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

机译：基于综合权重策略特征学习的滚动轴承智能故障诊断新方法

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摘要

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.
机译：长期以来，人们一直在研究智能方法来进行故障诊断。传统上，特征提取和故障分类是分开的，并且此过程并不完全智能。此外，大多数传统的智能方法使用单个模型，当机器在复杂条件下工作时，该模型无法提取出可区别的特征。针对传统智能故障诊断方法的不足，提出了一种基于集合稀疏自动编码器的智能轴承故障诊断方法。三种不同的稀疏自动编码器用作主要架构。为了提高鲁棒性和稳定性，采用了一种基于距离度量和标准偏差度量的新颖加权策略来分配三个稀疏自动编码的加权。 Softmax分类器用于对集成特征的故障类型进行分类。通过大量实验验证了该方法的有效性，与相关方法的比较以及对广泛使用的电机轴承数据集的研究证明了该方法的优越性。结果表明，检测准确度和标准偏差分别为99.71％和0.05％。

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