A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning
A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning
复制标题
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
DOI：
10.3390/s20061774
复制
发表时间：
2020-03-01
期刊：
影响因子：
3.9
通讯作者：
Zhou, Yan
中科院分区：
文献类型：
--
作者：
He, Jun;Ouyang, Ming;Zhou, Yan
智能方法在故障诊断中的研究由来已久。传统的特征提取和故障分类是分离的，这一过程并不完全智能化。此外，大多数传统的智能方法使用的是个体模型，当机器在复杂的条件下工作时，无法提取出有区别的特征。针对传统智能故障诊断方法的不足，提出了一种基于集成稀疏编码器的轴承故障智能诊断方法。三种不同的稀疏自动编码器被用作主要架构。为提高算法的鲁棒性和稳定性，采用一种基于距离度量和标准差度量的权值分配策略对三种稀疏自编码进行权值分配。采用Softmax分类器对综合特征进行故障类型分类。通过大量的实验验证了该方法的有效性，并与相关方法进行了比较，同时对广泛使用的电机轴承数据集进行了研究，验证了该方法的优越性。结果表明，该方法的测试准确度为99.71%，标准偏差为0.05%。
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%.