基于深度学习的机械设备退化状态建模及剩余寿命预测研究

Abstract

With the development of science and technology, analyzing the huge amounts of data produced by mechanical equipment to provide some useful decision-making information for the independent health management and maintenance has become one of the important industrial tasks. The degradation state modeling and remaining useful life (RUL) prediction are core contents. Traditional methods rely too much on signal processing technologies and expertise, and the prediction accuracy needs to be improved when dealing with complex time series, which cannot meet the accurate and efficient equipment health management requirements gradually. Thus, looking for better mechanical equipment degradation state modeling and RUL prediction methods becomes more and more important. As a new algorithm in recent years, deep learning has made brilliant achievements in many fields with its powerful high-level feature extraction and nonlinear mapping ability, but the application in the field of health management of mechanical equipment is still to be further explored. In order to solve the problems in traditional methods, the degradation state modeling method based on deep learning is deeply studied in this thesis, and the RUL prediction for mechanical equipment is further researched based on obtained degradation curves.

This thesis starts from two conditions of one-dimensional and multi-dimensional monitoring data of mechanical equipment, and the bearing and turbofan engine are taken as examples to carry out the data-driven degradation state modeling and remaining useful life prediction. Firstly, the typical bearing vibration data are studied. On the basis of the advantages and disadvantages of existing deep learning models, the one-dimensional monitoring data degradation state modeling method based on stack denoising autoencoders (SDAE) and self-organizing mapping network is proposed, and the performance verification for the algorithm is conducted on PHM2012 dataset. Then this thesis takes the turbofan engine as an example and the multi-dimensional monitoring data degradation state modeling method based on SDAE is proposed, and the CMAPSS dataset is used for performance assessment. Finally, this thesis compares the existing RUL prediction models and ultimately chooses the Long Short-Term Memory (LSTM) network for mechanical equipment RUL prediction. The experimental verification is conducted on the basis of obtained degradation curves of bearing and turbofan engine.

The results show that, compared with traditional mechanical equipment degradation state modeling methods, the proposed method based on SDAE is able to

construct more smooth degradation curves with smaller noise on the one-dimensional and multi-dimensional monitoring data, and the degradation curves have better time correlation and monotonicity. The degradation state modeling method has less dependence on artificial participation, and the whole procedure is in an unsupervised manner, with better generality. On the basis of constructed degradation curves, the way that LSTM model maps the health values of bearing and turbofan engine directly into the RUL achieves excellent results, which further proves the necessity of degradation state modeling and effectiveness of the prediction model.

Keywords: Degradation state modeling; remaining useful life prediction; deep learning; bearing; turbofan engine

目录

摘要 ............................................................................................................................... I Abstract............................................................................................................................. I I

第1章绪论 (1)

1.1 课题背景及研究的目的和意义 (1)

1.2 机械设备退化状态建模研究现状 (2)

1.2.1 机械设备退化状态建模概述 (2)

1.2.2 机械设备退化状态建模国内外研究现状 (3)

1.2.3 机械设备退化状态建模存在的问题及分析 (6)

1.3 机械设备剩余寿命预测研究现状 (6)

1.3.1 机械设备剩余寿命预测概述 (7)

1.3.2 机械设备剩余寿命预测国内外研究现状 (7)

1.3.3 机械设备剩余寿命预测存在的问题及分析 (8)

1.4 本文研究内容与结构 (9)

第2章基于一维监测数据的轴承退化状态建模 (11)

2.1 轴承退化状态建模方法概述 (11)

2.2 面向轴承退化状态建模的深度学习模型选择 (12)

2.2.1 深度学习方法简介 (13)

2.2.2 堆叠去噪自编码器原理 (14)

2.2.3 去噪自动编码机特征提取能力研究 (16)

2.3 基于SDAE和SOM的轴承退化状态建模方法 (19)

2.3.1 基于SDAE和SOM的轴承退化状态建模框架 (19)

2.3.2 轴承退化状态建模具体流程 (20)

2.4 实验验证及分析 (24)

2.4.1 PHM2012轴承退化数据介绍及预处理结果 (24)

2.4.2 轴承健康因子评价指标 (27)

2.4.3 实验设置及结果 (27)

2.4.4 与RMS、PCA、ELM_AE方法对比及分析 (29)

2.5 本章小结 (34)

第3章基于多维监测数据的涡轮发动机退化状态建模 (35)

3.1 涡轮发动机退化状态建模方法概述 (35)

3.2 基于SDAE的涡轮发动机退化状态建模方法 (36)

3.2.1 深度学习模型选择 (37)

3.2.2 基于SDAE的涡轮发动机退化状态建模框架 (37)

3.2.3 涡轮发动机退化状态建模具体流程 (38)

3.3 实验验证及分析 (39)

3.3.1 CMAPSS发动机数据介绍及预处理结果 (39)

3.3.2 涡轮发动机健康因子评价指标 (42)

3.3.3 实验设置及结果 (43)

3.3.4 与PCA、ELM_AE方法对比及分析 (44)

3.4 本章小结 (47)

第4章基于LSTM的机械设备剩余寿命预测方法 (48)

4.1 机械设备剩余寿命预测概述 (48)

4.1.1 机械设备剩余寿命预测流程简介 (48)

4.1.2 机械设备剩余寿命预测评价指标 (49)

4.1.3 基于深度学习的机械设备剩余寿命预测方法回顾 (51)

4.1.4 面向机械设备剩余寿命预测的深度学习模型选择 (52)

4.2 基于LSTM的轴承剩余寿命预测方法 (53)

4.2.1 轴承数据描述及处理 (53)

4.2.2 基于LSTM的轴承剩余寿命预测流程 (53)

4.2.3 实验验证及分析 (57)

4.3 基于LSTM的涡轮发动机剩余寿命预测方法 (58)

4.3.1 涡轮发动机数据描述及处理 (58)

4.3.2 基于LSTM的涡轮发动机剩余寿命预测流程 (59)

4.3.3 实验验证及分析 (60)

4.4 本章小结 (63)

结论 (64)

参考文献 (65)

攻读学位期间发表的学术论文 (76)

哈尔滨工业大学学位论文原创性声明和使用权限 (77)

致谢 (78)