Vehicle-mounted antenna transmission system health assessment method based on multi-feature fusion SOM

Abstract

The invention discloses a vehicle-mounted antenna transmission system health assessment method based on multi-feature fusion SOM, and the method comprises the steps: collecting the operation data of a radar vehicle antenna transmission system, and screening feature indexes; decomposing the operation data of the radar vehicle antenna transmission system, and calculating the energy ratio of a low-frequency-band fault characteristic frequency band; taking the characteristic value and the energy ratio as characteristic input, and training a self-organizing mapping neural network to obtain an optimal matching unit; the operation data of the radar vehicle antenna transmission system under various working conditions are input into the self-organizing mapping neural network, a weight vector is output, and if the Euclidean distance between the weight vector and the optimal matching unit is smaller than a threshold value, the operation state of the radar vehicle antenna transmission system is normal; otherwise, the operation state of the radar vehicle antenna transmission system is abnormal. According to the method, the complexity of fault diagnosis of a single part is overcome, online fault early warning of the whole transmission system can be achieved, high accuracy can be achieved, and meanwhile, the method has guiding significance for equipment maintenance personnel and maintenance strategy making.

Description

technical field [0001] The invention belongs to the field of state monitoring and fault diagnosis of mechanical equipment, and in particular relates to a health assessment method of a vehicle-mounted antenna transmission system based on a multi-feature fusion SOM. Background technique [0002] Military radar vehicle is the hub of military command and dispatch, and it is the "clairvoyance" and "wind ear" of commanders. In order to ensure the normal use and all-round coverage of the antenna detection function of the radar vehicle, the transmission mechanism that controls the lifting and rotating of the antenna is very important. However, due to the harsh battlefield environment, volatile weather conditions, variable temperature, and bumpy roads, the antenna transmission system may be damaged, resulting in inaccurate angle feedback control and affecting the use of the antenna. Therefore, the status monitoring and fault diagnosis research on the radar vehicle antenna transmissi...

AI Technical Summary

Problems solved by technology

However, the radar vehicle antenna transmission system is a complex nonlinear system, which contains key components such as worm gears, gears, bearings, motors, and shafts. It is difficult for a single signal feature to effectively reflect the health of the entire system; at the same time, the commonly used The fault classification method of machine learning and neural network requires a large amount of fault data with labels, and radar vehicles generally have few faults during operation, and there are many normal data, but it is difficult to obtain fault data, and there are fewer fault data with labels.

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