基于随机邻域嵌入的无监督复杂工况识别

doi:10.16182/j.issn1004731x.joss.23-0336

摘要/Abstract

摘要：

现代工业生产设备通常结构复杂并交替运行于不同工况，基于监测数据进行准确的工况识别是对系统进行健康监测的基础，但系统的监测数据通常维度较高、数据量较大。针对设备复杂工况的识别问题，提出了一种基于随机邻域嵌入的无监督工况识别方法。采用随机邻域嵌入算法，能够保留数据的局部和全局结构特性；计算了高维和低维空间中数据点的概率相似性，可实现设备高维监测数据的降维和无监督聚类，在不建立系统模型的基础上达成准确识别系统工况的目的。结果表明：该方法可有效实现高维监测数据的复杂工况识别，是一种有效的无监督聚类学习方法。

关键词: 随机邻域嵌入, 无监督, 工况识别, 降维, 聚类

Abstract:

Modern industrial production equipment usually has a complex structure and runs alternately in different working conditions. Accurate working conditions identification based on monitoring data is the basis of health monitoring of the system, but the monitoring data of the system usually has a high dimension and a large data volume. To identify the complex equipment operating conditions, an unsupervised operating condition identification method based on stochastic neighborhood embedding is proposed. The stochastic neighborhood embedding algorithm can simultaneously preserve the local and global structural characteristics of the data, and also calculate the probability similarity of data points in high-dimensional and low-dimensional space to achieve the dimensionality reduction and unsupervised clustering of equipment high-dimensional monitoring data and to accurately identify the system operating conditions without establishing a system model. The results show that the proposed method can effectively identify complex operating conditions from high-dimensional monitoring data, which is an effective unsupervised clustering learning method.

Key words: stochastic neighborhood embedding, unsupervised learning, working condition identification, dimensionality reduction, clustering

中图分类号:

TP391.9

黄林,刘善君,王伟等 . 基于随机邻域嵌入的无监督复杂工况识别[J]. 系统仿真学报, 2024, 36(6): 1334-1343.

Huang Lin,Liu Shanjun,Wang Wei,et al . Unsupervised Complex Condition Recognition Based on Stochastic Neighborhood Embedding[J]. Journal of System Simulation, 2024, 36(6): 1334-1343.

图/表 13

图1

图2

表1

表2

图3

图4

图5

图6

图7

表3

图8

表4

不同样本量下算法的表现

样本量(循环)	最优 $P e r p (P i)$	HI	CI	NMI	SI
5 000	58	0.984	0.983	0.984	0.982
10 000	201	0.967	0.963	0.965	0.951
20 000	258	0.958	0.952	0.955	0.933
30 000	374	0.956	0.951	0.954	0.927
40 000	581	0.951	0.945	0.948	0.914
50 000	865	0.948	0.943	0.945	0.933

表4

图9

参考文献 24

1	黄林, 龚立, 姜伟, 等. 基于多源信息融合与HMM的剩余寿命预测[J]. 系统工程与电子技术, 2022, 44(5): 1747-1756.
	Huang Lin, Gong Li, Jiang Wei, et al. Remaining Useful Life Prediction Based on Multi-source Information Fusion and HMM[J]. Systems Engineering and Electronics, 2022, 44(5): 1747-1756.
2	Wu R T, Jahanshahi M R. Data Fusion Approaches for Structural Health Monitoring and System Identification: Past, Present, and Future[J]. Structural Health Monitoring, 2020, 19(2): 552-586.
3	Huang Lin, Gong Li, Chen Yutao, et al. Trajectory Similarity Matching and Remaining Useful Life Prediction Based on Dynamic Time Warping[J]. Mathematical Problems in Engineering, 2022, 2022: 5344461.
4	Sammaknejad N, Zhao Yujia, Huang Biao. A Review of the Expectation Maximization Algorithm in Data-driven Process Identification[J]. Journal of Process Control, 2019, 73: 123-136.
5	Zhang Weiting, Yang Dong, Wang Hongchao. Data-driven Methods for Predictive Maintenance of Industrial Equipment: A Survey[J]. IEEE Systems Journal, 2019, 13(3): 2213-2227.
6	张安安, 黄晋英, 冀树伟, 等. 基于卷积神经网络图像分类的轴承故障模式识别[J]. 振动与冲击, 2020, 39(4): 165-171.
	Zhang Anan, Huang Jinying, Ji Shuwei, et al. Bearing Fault Pattern Recognition Based on Image Classification with CNN[J]. Journal of Vibration and Shock, 2020, 39(4): 165-171.
7	姚鹏川. 基于数据驱动的核动力装置状态监测方法研究[J]. 核动力工程, 2020, 41(增1): 135-139.
	Yao Pengchuan. Research on Condition Monitoring Method for Nuclear Power Plants Based on Data Drive[J]. Nuclear Power Engineering, 2020, 41(S1): 135-139.
8	Wang Yu, Liu Miao, Yang Jie, et al. Data-driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 4074-4077.
9	Roy Saurav, Ray Ratula, Satya Ranjan Dash, et al. Plant Disease Detection Using Machine Learning Tools with an Overview on Dimensionality Reduction[M]//Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, et al. Data Analytics in Bioinformatics: A Machine Learning Perspective. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2021: 109-144.
10	Zebari Rizgar R, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, et al. A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction[J]. Journal of Applied Science and Technology Trends, 2020, 1(2): 56-70.
11	Laurens van der Maaten, Hinton G. Visualizing Data Using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579-2605.
12	尚前明, 黄兴烨, 沈栋, 等. 基于改进t-SNE和RBFNN的柴油机故障诊断[J]. 船舶工程, 2023, 45(1): 91-97.
	Shang Qianming, Huang Xingye, Shen Dong, et al. Fault Diagnosis of Diesel Engine Based on Improved t-SNE and RBFNN[J]. Ship Engineering, 2023, 45(1): 91-97.
13	王金刚, 徐航, 刘海, 等. 基于t-SNE与模糊聚类的电动汽车行驶工况构建[J]. 重庆交通大学学报(自然科学版), 2022, 41(6): 126-132, 146.
	Wang Jingang, Xu Hang, Liu Hai, et al. Driving Construction of Electric Vehicles Based on t-SNE and Fuzzy Clustering[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41(6): 126-132, 146.
14	Song Weijing, Wang Lizhe, Liu Peng, et al. Improved t-SNE Based Manifold Dimensional Reduction for Remote Sensing Data Processing[J]. Multimedia Tools and Applications, 2019, 78(4): 4311-4326.
15	Pouyet E, Rohani N, Katsaggelos A K, et al. Innovative Data Reduction and Visualization Strategy for Hyperspectral Imaging Datasets Using t-SNE Approach[J]. Pure and Applied Chemistry, 2018, 90(3): 493-506.
16	华强. 基于t-SNE的虚拟样本生成技术研究及建模应用[D]. 北京: 北京化工大学, 2022.
	Hua Qiang. Research of Virtual Sample Generation Technology Based on t-SNE and Modeling Application[D]. Beijing: Beijing University of Chemical Technology, 2022.
17	Abdi Hervé, Williams L J. Principal Component Analysis[J]. WIREs Computational Statistics, 2010, 2(4): 433-459.
18	Huang Hong, Shi Guangyao, He Haibo, et al. Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning[J]. IEEE Transactions on Cybernetics, 2020, 50(6): 2604-2616.
19	Aremu O O, Hyland-Wood D, McAree P R. A Machine Learning Approach to Circumventing the Curse of Dimensionality in Discontinuous Time Series Machine Data[J]. Reliability Engineering & System Safety, 2020, 195: 106706.
20	Wen Tao, Duan Shuyu, Jiang Wen. Node Similarity Measuring in Complex Networks with Relative Entropy[J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 78: 104867.
21	Saxena A, Goebel K, Simon D, et al. Damage Propagation Modeling for Aircraft Engine Run-to-failure Simulation[C]//2008 International Conference on Prognostics and Health Management. Piscataway, NJ, USA: IEEE, 2008: 1-9.
22	Rosenberg A, Hirschberg J. V-measure: A Conditional Entropy-based External Cluster Evaluation Measure[C]//Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). Stroudsburg, PA, USA: ACL, 2007: 410-420.
23	McDaid A F, Greene D, Hurley N. Normalized Mutual Information to Evaluate Overlapping Community Finding Algorithms [EB/OL]. [2023-04-23]. .
24	Rousseeuw Peter J. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20: 53-65.

序号	符号	描述
1	T2/°C	风扇入口总温
2	T24/°C	低压压气机出口总温
3	T30/°C	高压压气机出口总温
4	T50/°C	低压涡轮出口总温
5	P2/kPa	风扇入口压力
6	P15/kPa	外涵总压
7	P30/kPa	高压压气机出口总压
8	Nf/(r/min)	风扇物理转速
9	Nc/(r/min)	核心机物理转速
10	epr	发动机压比(P50/P2)
11	Ps30/kPa	高压压气机出口静压
12	Phi/(kg·s^-1/kPa)	燃油流量与P30比值
13	NRf/(r/min)	风扇换算转速
14	NRc/(r/min)	核心机换算转速
15	BPR	涵道比
16	farB	燃烧室燃气比
17	htBleed	引气焓值
18	Nf_dmd/(r/min)	设定风扇转速
19	PCNfR_dmd/(r/min)	设定核心机换算转速
20	W31/(kg/s)	高压涡轮冷却引气流量
21	W32/(kg/s)	低压涡轮冷却引气流量

数据集	运行工况	故障模式	训练集数量
FD001	1	1	100
FD002	6	1	260
FD003	1	2	100
FD004	6	2	249

算法	HI	CI	NMI	SI
PCA	0.644	0.675	0.659	0.584
LLE	0.783	0.832	0.807	0.722
t-SNE	0.936	0.929	0.932	0.910

[1]	卫升, 王艳, 纪志成. 多工况生产过程下的即时学习能耗预测建模方法[J]. 系统仿真学报, 2024, 36(6): 1378-1391.
[2]	肖鹏, 谢锋, 倪海鸿, 张敏, 汤志荔, 李霓. 多机任务分配与路径规划协同优化法研究[J]. 系统仿真学报, 2024, 36(5): 1141-1151.
[3]	罗宇航, 吴润秀, 崔志华, 张翼英, 何业慎, 赵嘉. 基于密度峰值聚类的Tri-training算法[J]. 系统仿真学报, 2024, 36(5): 1189-1198.
[4]	赵彦霖, 田云娜. 基于K-means聚类的超启发式跨单元调度方法[J]. 系统仿真学报, 2024, 36(4): 941-956.
[5]	邵绪强, 程雅, 金佚钟. 基于聚类融合的三维流线可视化方法[J]. 系统仿真学报, 2024, 36(3): 625-635.
[6]	陈静, 张昭冲, 王琳凯, 安脉, 王伟. 基于卷积长短时记忆网络的短时公交客流量预测[J]. 系统仿真学报, 2024, 36(2): 476-486.
[7]	丁飞, 张美楠, 庄衡衡, 马海蓉, 张登银. 极地灾害区域监测的目标搜索规划与算法[J]. 系统仿真学报, 2023, 35(7): 1526-1538.
[8]	吴铮, 张悦, 董泽. 基于改进高斯混合模型的热工过程异常值检测[J]. 系统仿真学报, 2023, 35(5): 1020-1033.
[9]	赵拓, 邓汉强, 高佳隆, 黄健. 基于网络节点聚类的多无人机动态目标分配[J]. 系统仿真学报, 2023, 35(4): 695-708.
[10]	何一帆, 何玉林, 蔡湧达, 黄哲学. 基于夹角几何的I-niceMO增强算法[J]. 系统仿真学报, 2023, 35(4): 797-808.
[11]	王旭, 季伟东, 周国辉, 杨佳慧. 基于多指标精英个体博弈机制的多目标优化算法[J]. 系统仿真学报, 2023, 35(3): 494-514.
[12]	范厚明, 咸富山, 王怀奇. 动态需求下考虑订单聚类的外卖配送路径优化[J]. 系统仿真学报, 2023, 35(2): 396-407.
[13]	陈雪, 胡蓉, 王辉, 李作成, 钱斌, 李熠胥. 学习型蚁群算法求解一类复杂两级车辆路径问题[J]. 系统仿真学报, 2023, 35(11): 2476-2495.
[14]	季伟东, 岳玉麒, 王旭, 林平. 基于降维和聚类的大规模多目标自然计算方法[J]. 系统仿真学报, 2023, 35(1): 41-56.
[15]	周培培, 侯幸林. 一种用于图像融合的无监督深度神经网络[J]. 系统仿真学报, 2022, 34(6): 1267-1274.