Research On The Autoloader Device’Fault Diagnostic Mechanism Based On The Current Detection

Automatic loader is one of the important part of the artillery autoloader device thesystem. The agency’s work is normal or not that directly related to the whole system operationstate, and even the development of entire situation. It has a very important significance thatclassifies and recognizes the fault through timely finding faults and monitoring the state. Itcan also avoid unnecessary trouble and loss and provide a powerful guarantee for the victoryof war. The work environment of automatic loader is very bad and the vibration is alsoobvious, which leads to the background noise in the signal is quite large. A part of the usefulsignals may be covered by strong noise. The traditional signal analysis methods may notreach the expected effect of the diagnosis.Here introduced current signal analysis, as MCSA. It uses induction motor statorcurrent as a signal analysis the point, and studies the characteristics and the correspondingrelation of failure. As a new method of fault diagnosis, it used in some other meansinconvenience or easily found the problem the implementation of the occasion. This paper’sprocessing method is real-time monitoring the four sets of dc servo motor operation by hallcurrent sensor. And it can also transfer the electrical signals to DASP signal collection systemfor post processing. In the later processing, it can classify all kinds of signal under thecondition, and extract current signal characteristic parameters which are related. Finally it canuse BP neural network to classify and diagnosis for fault.Base on theory in the basis of the analysis, we can analysis the electrical signals of thecollection of laboratory dc servo motor. And then it can use BP neural network to classify anddiagnosis for fault. Neural network is a kind of adaptive pattern recognition technology, it isused extensively in pattern recognition of the fault and the theoretical study is more mature.