Unsupervised Complex Condition Recognition Based on Stochastic Neighborhood Embedding

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

Abstract

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

CLC Number:

TP391.9

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

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

Fig. 8

Table 4

Performance of algorithm under different sample sizes

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

Table 4

Fig. 9

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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

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