|
According to the national military standard, the long-term stored fuzes need to be inspected and sampled regularly. However, the circuit signal of the fuze is difficult to extract for detection, because the fuze electronic safety and arming device(ESAD) in the weapons inventory product is fully packaged. It is a common and effective method to realize equipment health monitoring by detecting vibration signal and analyzing its characteristics. However, the vibration signal of the electronic circuit system is extremely weak, and there is the problem of electromagnetic interference caused by high voltage circuit transient large current. Optical fiber sensor has the advantages of anti-electromagnetic interference, long transmission distance, and high sensitivity. Meanwhile, deep learning method has the advantage of automatically extracting data features and classifying them. This paper combines the advantages of optical fiber sensor and deep learning to diagnose the fuze ESAD. A sensing probe formed by a weak reflectivity FBG pair was used to detect the weak vibration signal during the operation of the fuze ESAD, and the deep learning model was constructed to realize the recognition of stop, start and five typical failure modes of ESAD, with the recognition accuracy of 99.3%. It can provide an effective solution for the diagnosis and evaluation of the high-voltage circuit state of ESAD.
|
