A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

Ge Ming-Feng; Ge Ziyue; Pan Hao; Liu Yiben; Xu Yanhe; Liu Jie

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A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

机译：基于MMSDE和优化SAE的旋转机械旋转机械的深度条件特征学习方法

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摘要

The failure of rotating machinery affects the quality of the product and the entire production process. However, it usually suffers the subsequent deficiency that the hyperparameters of the fault diagnosis model require constant debugging. This paper proposes a deep condition feature learning approach for rotating machinery based on modified multi-scale symbolic dynamic entropy (MMSDE) and optimized stacked auto-encoders (SAEs). Firstly, MMSDE has been used to extract fault characteristics of the original vibration signal, because such methods do not rely on prior knowledge and experience. MMSDE conducts multi-scale analysis on the original vibration signal and calculates the entropy of the multi-scale signal. The multi-scale fault characteristics are obtained. Then, Bayesian optimization-based SAEs are applied to select feature samples and classify the fault status in mechanical fault diagnosis without debugging. The effectiveness of the proposed method is verified by using open-source data and experimental data. Multiple working conditions are also considered and investigated.

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来源
《Measurement Science & Technology》 |2021年第3期|共9页
作者
Ge Ming-Feng; Ge Ziyue; Pan Hao; Liu Yiben; Xu Yanhe; Liu Jie;
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作者单位

China Univ Geosci Sch Mech &

Elect Informat Wuhan 430074 Peoples R China;

China Univ Geosci Sch Mech &

Elect Informat Wuhan 430074 Peoples R China;

Huazhong Univ Sci &

Technol Sch Artificial Intelligence &

Automat Wuhan 430074 Peoples R China;

China Univ Geosci Sch Mech &

Elect Informat Wuhan 430074 Peoples R China;

Huazhong Univ Sci &

Technol Sch Civil &

Hydraul Engn Wuhan 430074 Peoples R China;

Huazhong Univ Sci &

Technol Sch Civil &

Hydraul Engn Wuhan 430074 Peoples R China;

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原文格式 PDF
正文语种 eng
中图分类仪器、仪表;
关键词
rotating machinery; modified multi-scale symbolic dynamic entropy; stacked auto-encoders; fault diagnosis; multiple working conditions;

机译：旋转机械;改进的多尺度符号动态熵;堆叠自动编码器;故障诊断;多个工作条件;

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3. Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis [J] . 公茂法, 刘庆雪, 刘明, 测试科学与仪器：英文版 . 2010,第004期
4. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis [J] . Liu Jie, Hu Youmin, Wang Yan, Measurement Science & Technology . 2018,第5期

机译：基于多传感器融合的深度学习方法，用于旋转机械诊断
5. Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach [J] . Ding Yu, Ma Liang, Ma Jian, Advanced engineering informatics . 2019,第Octa期

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6. Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection [J] . Dongying Han, Kai Liang, Peiming Shi Brazilian Journal of Operations and Production Management . 2020,第4期

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8. A Hybrid Deep Learning Approach for Time Series Prediction based on Unsupervised Feature Learning [D] . Agana, Norbert Ayine. 2018

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9. A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery [O] . Funa Zhou, Po Hu, Shuai Yang, 2018

机译：基于多峰特征融合的深度学习方法在旋转机械在线故障诊断中的应用
10. A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery [O] . Funa Zhou, Po Hu, Shuai Yang, 2018

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11. Software-Based Controller for Generation of Synchronized Pulses for Use with Rotating Machinery. [R] . zur Loye, A. O. 1989

机译：用于生成与旋转机械同步脉冲的基于软件的控制器。

A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

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