面向工业大数据的时间序列预测关键技术研究

Abstract

In recent years, with the advent of the Industry 4.0, industrial big data has become an important research area. Due to the complex production process, a large number of sensors, rapid sampling frequency, and industrial equipment can easily accumulate large amounts of data in a short period, industrial big data has features like complicated mechanism models, time-series data, strong data dependence, high data dimensions, and massive data without labels. When there are special operating conditions, it often has large economic losses, so if an accurate prediction of the abnormalities in the production process is made, it will increase efficiency of the entire production process, contribute to large practical value.

This topic mainly target at modeling algorithms for time series data of industrial big data. The traditional analysis methods of industrial big data often emphasize the statistical model and ignores the time correlation of industrial data. Therefore, from the perspective of the time sequence of data, this paper proposes time series data prediction algorithms from three aspects. The main work of this paper includes: First, a yield prediction algorithm based on time series data - a LSTM algorithm based on multi-variable tuning is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences using periodicity, improving the prediction accuracy.

Secondly, a fault prediction algorithm based on transfer learning-a transfer learning algorithm based on time window is brought up. This algorithm puts forward the concept of time window, using transfer learning between different machines with different sampling frequency, solving the learning problem of data without labels in industrial data.

Thirdly, a prediction algorithm based on lifelong learning –a data update model is proposed for the lifelong learning prediction algorithm. It updates the existing model combining the data update model to automatically update model parameters over time. It can effectively identify the changes of data and iteratively update the model.

To sum up, we have established a time series forecasting model system, which solves the problems of continuous variable prediction, discrete variable prediction

Abstract

and model self-learning updating in time series data. It mainly includes the following innovations: introducing the concept of periodic measurement and time window in the industrial prediction problem, especially for ndustrial data with time series characteristics; introducing transfer learning into time series data of different devices in the same production process; establishing data update model replacing mechanism model to simplify model update.

Experimental results show that the time-series yield prediction algorithm improves the prediction accuracy by 54.05%, the transfer learning based fault prediction algorithm has a transfer accuracy about 97%, and lifelong learning prediction algorithm can update the data model effectively with at least 33% accuracy.

Keywords: Time series prediction, Fault prediction, LSTM, Transfer learning, Lifelong learning, Neural network

摘要 .......................................................................................................................... I ABSTRACT ................................................................................................................ II 第1章绪论 .. (1)

1.1课题来源 (1)

1.2研究背景和意义 (1)

1.3国内外研究现状及分析 (2)

1.3.1 国外研究现状分析 (2)

1.3.2 国内研究现状分析 (3)

1.4本文的主要研究内容 (5)

1.5本文的组织结构 (6)

第2章预备知识 (8)

2.1引言 (8)

2.2时间序列预测 (8)

2.2.1 长短期记忆网络LSTM (9)

2.3迁移学习 (10)

2.3.1 迁移学习基础概念 (11)

2.3.2 基于神经网络的迁移学习 (11)

2.4终身机器学习 (12)

2.5本章小结 (13)

第3章基于时间序列分析的流量预测 (14)

3.1引言 (14)

3.2问题描述 (14)

3.3多变量调优的LSTM算法 (15)

3.3.1 数据转换模块 (15)

3.3.2 LSTM建模模块 (17)

3.3.3 调优模块 (18)

3.4算法描述与分析 (19)

3.5实验结果 (21)

3.5.1 预测型问题的算法评价方式 (21)

3.5.2 实验搭建 (21)

3.5.4 多变量调优的LSTM算法调优 (22)

3.5.5 对比实验结果 (25)

3.6本章小结 (26)

第4章基于迁移学习的故障预测 (27)

4.1引言 (27)

4.2问题描述 (27)

4.3近似算法 (28)

4.4基于时间窗口的迁移学习算法 (28)

4.4.1 时间窗口模块 (29)

4.4.2 映射网络模块 (31)

4.4.3 模型迁移模块 (32)

4.5算法描述与分析 (33)

4.6实验结果 (35)

4.6.1 分类型问题的算法评价方式 (35)

4.6.2 实验搭建 (36)

4.6.3 映射网络优化 (37)

4.6.4 迁移学习实验结果 (38)

4.7本章小结 (40)

第5章面向终身学习的预测 (41)

5.1引言 (41)

5.2问题描述 (41)

5.3基于数据更新模型的终身学习预测算法 (42)

5.3.1 数据相似性模块 (43)

5.3.1 损失函数模块 (44)

5.4算法描述与分析 (46)

5.5实验结果 (48)

5.5.1 实验搭建 (48)

5.5.2 基于数据更新模型的终身学习预测算法优化 (48)

5.5.3 基于时间序列数据的流量预测算法实验结果 (50)

5.5.4 基于迁移学习的故障预测算法实验结果 (51)

5.6本章小结 (52)

结论 (53)

参考文献 (55)

攻读硕士学位期间发表的论文及其它成果 (61)

哈尔滨工业大学学位论文原创性声明和使用权限 (62)

致谢 (63)