文献类型：期刊文献

英文题名：Rolling Bearing Initial Fault Detection Using Long Short-Term Memory Recurrent Network

作者：Shi, Huaitao[1];Guo, Lei[1];Tan, Shuai[2];Bai, Xiaotian[1]

机构：[1]Shenyang Jianzhu Univ, Sch Mech Engn, Shenyang 110168, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2019

卷号：7

起止页码：171559

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200408087755);WOS:【SCI-EXPANDED(收录号:WOS:000509374200017)】；

基金：This work was supported in part by the National Key Research and Development Plan under Gant 2017YFC0703903, in part by the National Natural Science Foundation of China under Gant 51705341 and Gant 51905357, and in part by the Natural Science Foundation of Liaoning under Gant 2019-ZD-0654.

语种：英文

外文关键词：Rolling bearing; fault diagnosis; long-short-term memory

摘要：The complete failure of the rolling bearing is a deterioration process from the initial minor fault to the serious fault, it is meaningless for guiding maintenance when the serious fault is alarmed. This work presents a novel initial fault diagnosis framework based on sliding window stacked denoising auto-encoder (SDAE) and long short-term memory (LSTM) model. In this approach, multiple vibration value of the rolling bearings are entered into SDAE by sliding window processing. Then, multiple vibration value of the rolling bearings of the next period is predicted from the signal reconstructed by the trained SDAE in the previous period using LSTM. For the given input data, the reconstruction errors between the next period data and the output data generated by trained LSTM are used to detect initial anomalous conditions. The proposed method not only utilizes the ability of SDAE to learn the inherent distribution of data, but also ensures that LSTM can extract timing relationships between data cycles, and the model is built using only normal data. The initial fault detection as a key difficulty in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. Experimental and classic rotating machinery datasets have been employed to testify the effectiveness of the proposed method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method can effectively detect the initial anomalies of the rolling bearing and accurately describe the deterioration trend with strong robustness, and have high significance for maintenance guiding.