%0 Journal Article %T 基于深度学习的缓变故障早期诊断及寿命预测<br>Early diagnosis and life prognosis for slowlyvarying fault based on deep learning %A 周福娜 %A 高育林 %A 王佳瑜 %A 文成林< %A br> %A ZHOU Funa %A GAO Yulin %A WANG Jiayu %A WEN Chenglin %J 山东大学学报（工学版） %D 2017 %R 10.6040/j.issn.1672-3961.0.2017.193 %X 摘要： 为了克服传统的早期微小故障诊断方法不能区分多个不同时刻发生故障的不足,提出一种将深度学习和PCA相结合的方法实现微小缓变故障早期诊断及寿命预测。 对采集的数据进行深度学习实现逐层特征抽取,学习早期微小故障特征,建立微小缓变故障早期诊断模型,结合PCA方法将深度学习所抽取的高维故障特征向量集成为一个故障特征变量,根据历史故障数据特征变量演化规律定义数据驱动的故障演变标尺,并通过指数型非线性拟合方法建立寿命预测模型。 选取TE平台数据进行算法有效性检验,并与其他算法对比,从而验证了所提出算法的有效性。<br>Abstract: In order to overcome the shortcoming of traditional early fault diagnosis methods, a method of combining deep learning with PCA to realize early diagnosis of slowly varying small fault and life prognosis was proposed. Using the deep learning method to extract the sampled data characteristics layer by layer, learning the early fault characteristics and establishing the early fault diagnosis model for slowly varying small fault, the combining deep learning was combined with PCA to integrate the high dimensional fault feature vector extracted by the deep learning into a fault characteristic variable. A data-driven fault precursor could be defined according to the evolution rule of the characteristic variable of the historical fault data, and life prognosis model was established by exponential nonlinear fitting method. The TE benchmark data was used to verify the effectiveness of the proposed algorithm, experimental results showed the validity of the proposed algorithm by comparing with other algorithms %K 缓变故障 %K 深度学习 %K 非线性拟合 %K 寿命预测 %K 早期诊断 %K < %K br> %K early diagnosis %K life prognosis %K slowly varying faults %K nonlinear fitting %K deep learning %U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.193