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论文:2021,Vol:39,Issue(2):407-413 |
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引用本文： |
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刘畅, 陈雯柏. 一种基于MSDCNN-LSTM的设备RUL预测方法[J]. 西北工业大学学报 |
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LIU Chang, CHEN Wenbai. A RUL prediction method of equipments based on MSDCNN-LSTM[J]. Northwestern polytechnical university |
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| 一种基于MSDCNN-LSTM的设备RUL预测方法 |
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| 刘畅, 陈雯柏 |
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| 北京信息科技大学 自动化学院, 北京 100192 |
| 摘要: |
| 针对设备剩余使用寿命（RUL）预测过程中数据维度高，时间序列相关性信息难以充分考虑的实际应用需求，提出一种多尺度深度卷积神经网络和长短时记忆网络融合（multi-scale deep convolutional neural network and long short-term memory，MSDCNN-LSTM）的设备剩余寿命预测方法。对传感器数据进行标准化和滑动时间窗口处理得到输入样本；采用基于多尺度深度卷积神经网络（MSDCNN）提取空间详细特征，采用长短时记忆网络（LSTM）提取时间相关性特征以进行有效的预测。基于商用模块化航空推进系统仿真数据集的实验表明，相较于其他最新方法，文中提出的方法取得了较好的预测结果，尤其是对于故障模式和运行条件复杂的设备寿命预测需求，该方法效果明显。 |
| 关键词:
剩余使用寿命
多尺度深度卷积神经网络
长短时记忆网络
时间窗口
融合预测模型
仿真实验
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| A RUL prediction method of equipments based on MSDCNN-LSTM |
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| LIU Chang, CHEN Wenbai |
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| School of Automation, Beijing Information Science & Technology University, Beijing 100192, China |
| Abstract: |
| In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective. |
| Key words:
remaining useful life
multi-scale deep convolutional neural network
long short-term memory
sliding time window
hybrid prediction model
simulation experiment
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| 收稿日期: 2020-06-17
修回日期:
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| DOI: 10.1051/jnwpu/20213920407 |
| 基金项目: 北京市自然科学基金（4202026）资助 |
| 通讯作者: 陈雯柏(1975-),北京信息科技大学教授、硕士生导师,主要从事智能检测技术与传感器网络研究。e-mail:Chenwb03@126.com
Email：Chenwb03@126.com |
| 作者简介: 刘畅(1995-),女,北京信息科技大学硕士研究生,主要从事机器学习和剩余寿命预测研究。
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| 作者相关文章 |
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| 刘畅 在本刊中的所有文章 |
| 陈雯柏 在本刊中的所有文章 |
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参考文献: |
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[1] RODRIGUES L R. Remaining useful life prediction for multiple-component systems based on a system-level performance indicator[J]. IEEE/ASME Trans on Mechatronics, 2017, 23(1):141-150
[2] ELTHALJI I, JANTUNEN E. A summary of fault modelling and predictive health monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2015, 60:252-272
[3] QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220-234
[4] KHELIF R, CHEBELMORELLO B, MALINOWSKI S, et al. Direct remaining useful life estimation based on support vector regression[J]. IEEE Trans on Industrial Electronics, 2017, 64(3):2276-2285
[5] ZHANG C, LIM P, QIN A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics[J]. IEEE Trans Neural Netw Learn Syst, 2017, 28(10):2306-2318
[6] WANG B, LEI Y, LI N, et al. Deep separable convolutional network for remaining useful life prediction of machinery[J]. Mechanical Systems and Signal Processing, 2019, 134:106330
[7] BABU G S, ZHAO P, LI X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//International Conference on Database Systems for Advanced Applications, Cham, 2016:214-228
[8] LI X, DING Q, SUN J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172:1-11
[9] LI H, ZHAO W, ZHANG Y, et al. Remaining useful life prediction using multi-scale deep convolutional neural network[J]. Applied Soft Computing, 2020, 89:106113
[10] ZHENG S, RISTOVSKI K, FARAHAT A K, et al. Long short-term memory network for remaining useful life estimation[C]//IEEE International Conference on Prognostics and Health Management, 2017:88-95
[11] KONG Z, CUI Y, XIA Z, et al. Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics[J]. Applied Sciences, 2019, 9(19):4156
[12] XIA T, SONG Y, ZHENG Y, et al. An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation[J]. Computers in Industry, 2020, 115:103182
[13] KURATA G, RAMABHADRAN B, SAON G, et al. Language modeling with highway LSTM[C]//2017 IEEE Automatic Speech Recognition and Understanding Workshop, 2017:244-251
[14] SARANYA C, MANIKANDAN G. A study on normalization techniques for privacy preserving data mining[J]. International Journal of Engineering and Technology, 2013, 5(3):2701-2704 |
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