Fault Diagnosis Method of Modular Charge Feeding Mechanism Based on Transfer Learning

doi:10.12382/bgxb.2022.0767

Abstract

Abstract:

To solve the problem of fault diagnosis of the modular charge feeding mechanism under multiple working conditions, a fault diagnosis method based on transfer learning and singular value decomposition (SVD) was proposed. SVD was used for dimensionality reduction and noise reduction as means of preprocessing of the modular charge velocity data and for feature extraction. The transfer learning method based on the TrAdaBoost algorithm framework was adopted to synthesize limited test data and a large amount of simulation data to extract effective fault information. In the information, multiple base fault classifiers were built and integrated into a high-quality fault classifier. The experimental results showed that the proposed method has good adaptability to the fault data under multiple working conditions, which can obtain better diagnosis accuracy compared to the traditional machine learning strategy in the case of limited test data.

Key words: modular charge feeding mechanism, transfer learning, singular value decomposition, fault diagnosis

CLC Number:

TJ303.3

HUANG Wenkuan, QIAN Linfang, YIN Qiang, LIU Taisu. Fault Diagnosis Method of Modular Charge Feeding Mechanism Based on Transfer Learning[J]. Acta Armamentarii, 2023, 44(10): 2964-2974.

Figures/Tables 20

References 20

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组合序号	缩略组合名	分布占比/%
1	1块-失效	0.06
2	1块-变形	0.12
3	1块-健康	27.8
4	4块-失效	0
5	4块-变形	2.1
6	4块-健康	29.2
7	6块-失效	0.07
8	6块-变形	0.03
9	6块-健康	38.1

组合序号	缩略组合名	分布占比/%
1	1块-失效	0.06
2	1块-变形	0.12
3	1块-健康	27.8
4	4块-失效	0
5	4块-变形	2.1
6	4块-健康	29.2
7	6块-失效	0.07
8	6块-变形	0.03
9	6块-健康	38.1

组合	样本占比/%	组合	样本占比/%	组合	样本占比/%
1	5	4	5	7	5
2	5	5	5	8	5
3	20	6	20	9	30

组合	样本占比/%	组合	样本占比/%	组合	样本占比/%
1	5	4	5	7	5
2	5	5	5	8	5
3	20	6	20	9	30

组合	样本占比/%	组合	样本占比/%	组合	样本占比/%
1	5	4	5	7	5
2	5	5	5	8	5
3	20	6	20	9	30