基于重构误差和多块建模策略的kNN故障监测

doi:10.16182/j.issn1004731x.joss.21-0689

摘要/Abstract

摘要：

针对基于k近邻(k-nearest neighbor，kNN)的故障监测算法中，引发故障的异常信息易被正常信息淹没，导致故障检测不及时和报警率低的问题，利用自编码器和多块建模策略提出一种基于重构误差的kNN故障监测方法。该方法利用正常工况数据集训练自编码器模型，基于该模型进行重构误差提取以解决异常信息易被淹没的问题。进一步考虑微小偏移和振荡等故障特征，采用多块建模策略，对各子块分别计算统计量并融合检测。通过一个数值例子与田纳西-伊斯曼(Tennessee-Eastman，TE)过程进行仿真与分析，结果验证了所提方法的有效性与监测性能的提升。

关键词: k近邻, 重构误差, 故障监测, 信息提取, 多块建模

Abstract:

For the fault monitoring algorithm based on k-nearest neighbor (kNN), the abnormal information that caused the fault is easy to be overwhelmed by the normal operating condition information, which leads to the problem of untimely fault detection and low alarm rate. A kNN fault monitoring method based on reconstruction error is proposed using auto-encoder and multi-block modeling strategy. The method uses the normal working condition data set to train the auto-encoder model, and extracts the reconstruction error based on the model to solve the problem that abnormal information is easy to be overwhelmed. Further considering the fault characteristics such as micro-offset and oscillation, a multi-block modeling strategy is adopted to calculate statistics for each sub-block and merge the detection. Through a numerical example and Tennessee-Eastman (Tennessee-Eastman, TE) process simulation and analysis, the results verify the effectiveness of the proposed method and the improvement of monitoring performance.

Key words: k-nearest neighbor(kNN), reconstruction error, fault detection, information extraction, multi-block modeling

中图分类号:

TP 277

郑静, 熊伟丽, 吴晓东. 基于重构误差和多块建模策略的kNN故障监测[J]. 系统仿真学报, 2023, 35(1): 95-109.

Jing Zheng, Weili Xiong, Xiaodong Wu. kNN Fault Detection Based on Reconstruction Error and Multi-block Modeling Strategy[J]. Journal of System Simulation, 2023, 35(1): 95-109.

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方法	报警率	误报率
kNN	0.090	0
RE_kNN	0.872	0.04
RE_MBIkNN	0.981	0

故障编码	以原始数据为观测信息				以重构误差为观测信息
故障编码	子块1	子块2	子块3	BIC	子块1	子块2	子块3	BIC
1	0.996	0.998	0.001	0.995	0.996	0.995	0.976	0.995
2	0.983	0.988	0.000	0.984	0.986	0.989	0.978	0.984
3	0.013	0.326	0.001	0.054	0.092	0.195	0.119	0.150
4	0.975	0.999	0.005	0.999	0.999	0.999	0.117	0.999
5	0.260	0.809	0.002	0.381	0.999	0.999	0.336	0.999
6	1	0.999	0.004	0.999	0.999	0.999	0.911	0.999
7	1	0.999	0.007	0.999	0.999	0.999	0.554	0.999
8	0.976	0.993	0.006	0.979	0.979	0.984	0.969	0.976
9	0.020	0.290	0.005	0.051	0.084	0.162	0.135	0.132
10	0.418	0.815	0.000	0.618	0.737	0.860	0.393	0.848
11	0.683	0.941	0.047	0.868	0.758	0.930	0.553	0.913
12	0.989	1.000	0.199	0.993	0.995	1.000	0.980	1.000
13	0.946	0.963	0.031	0.951	0.950	0.958	0.944	0.953
14	1	0.878	0.998	0.998	0.999	0.998	0.999	0.999
15	0.029	0.326	0.004	0.105	0.101	0.220	0.119	0.181
16	0.289	0.820	0.002	0.549	0.729	0.886	0.291	0.893
17	0.919	0.974	0.149	0.966	0.946	0.975	0.864	0.976
18	0.896	0.940	0.041	0.899	0.906	0.914	0.898	0.913
19	0.099	0.412	0.114	0.135	0.669	0.644	0.697	0.779
20	0.495	0.848	0.039	0.642	0.669	0.843	0.452	0.810
21	0.425	0.699	0.001	0.566	0.576	0.664	0.583	0.639
平均报警率	0.639	0.810	0.079	0.701	0.770	0.820	0.613	0.816
平均误报率	0.006	0.159	0.003	0.015	0.046	0.079	0.067	0.059

故障编码	报警率
故障编码	PCA	SVDD	kNN	MBIkNN	RE_kNN	RE_MBIkNN
1	0.999	0.993	0.995	0.995	0.996	0.995
2	0.985	0.983	0.983	0.984	0.986	0.984
3	0.032	0.035	0.013	0.054	0.092	0.150
4	1	0.790	0.974	0.999	0.999	0.999
5	0.253	0.275	0.260	0.381	0.999	0.999
6	1	1	1	0.999	0.999	0.999
7	1	1	1	0.999	0.999	0.999
8	0.976	0.975	0.977	0.979	0.979	0.976
9	0.058	0.029	0.021	0.051	0.084	0.132
10	0.299	0.446	0.418	0.618	0.737	0.848
11	0.754	0.598	0.683	0.868	0.758	0.913
12	0.984	0.987	0.988	0.993	0.995	1
13	0.953	0.944	0.946	0.951	0.950	0.953
14	1	1	1	0.998	0.999	0.999
15	0.037	0.063	0.029	0.105	0.101	0.181
16	0.274	0.284	0.279	0.549	0.729	0.893
17	0.952	0.876	0.919	0.966	0.946	0.976
18	0.901	0.898	0.898	0.899	0.906	0.913
19	0.225	0.045	0.089	0.135	0.669	0.779
20	0.498	0.458	0.485	0.642	0.669	0.810
21	0.513	0.421	0.426	0.566	0.576	0.639
平均报警率	0.652	0.624	0.637	0.701	0.770	0.816
平均误报率	0.004	1.756	0.006	0.015	0.046	0.059

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