A Service Status Identification Method of Comprehensive Transmission Based on Multi-sensor Data Association

doi:10.12382/bgxb.2022.0664

Abstract

Abstract:

The sensor data jumps and the poor reliability of comprehensive transmission in harsh environment lead to a high false alarm rate in the state judgment brought by relying on a single sensor information, and make it difficult to accurately identify the service state. A service status identification method of comprehensive transmission based on multi-sensor data association is proposed. The proposed method fully considers the correlation degree of associated sensor data in each time period through the time window mean correlation network, and can effectively characterize the correlation relationship of sensor data under complex working conditions. Based on the calculation method of time window correlation degree, a back propagation (BP) data mapping model is constructed to complete the mapping of key sensor data. First, the variational mode decomposition-sample entropy (VMD-SE) method is used to preprocess the data; the correlation between the sensor data is calculated by the proposed method, and the data with high correlation is selected; finally, the data with high correlation is input into the constructed BP data mapping models to map the key sensor data. The case verification of oil pressure data is carried out. The results show that the time window correlation calculation method can accurately measure the correlation between sensor data, and the data output by the BP data mapping model can well characterize the key sensor data. The combination of the two can effectively improve the accuracy of service status judgment.

Key words: comprehensive transmission, status identification, data association, mapping model

CLC Number:

U463.22

XU Baorong, ZHANG Jinle, WAN Li, WU Haoyang, WANG Liyong. A Service Status Identification Method of Comprehensive Transmission Based on Multi-sensor Data Association[J]. Acta Armamentarii, 2024, 45(2): 574-583.

Figures/Tables 11

Fig.1 Linked data mapping model

Fig.2 Principle of mapping method

Fig.3 Flowchart of multi-sensor data mapping

Fig.4 Measuring points of comprehensive transmission

Fig.5 Test signal of comprehensive transmission

Fig.6 Sample data time window mean correlation network

Table 1 Mapping results of sensors with different correlation degrees

关联度	传感器数量	无干扰MSE	有干扰MSE
≥0.8	2	0.0090	0.0442
≥0.6	8	0.0065	0.0440
≥0.4	10	0.0069	0.0448
≥0.2	14	0.0124	0.0687

Fig.7 Manipulate the output results of oil pressure linked data model test set

Table 2 Mapping results of sensors with different correlation degrees

关联度	传感器数量	无干扰MSE	有干扰MSE
≥0.8	3	0.0042	0.0046
≥0.6	8	0.0034	0.0049
≥0.4	12	0.0039	0.0053
≥0.2	15	0.0125	0.0178

Fig.8 Test set output results of lubricating oil pressure correlation data model

Fig.9 Test set output results of two oil pressure linked data models

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