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 the system. The agency’s work is normal or not that directly related to the whole system operation state, and even the development of entire situation. It has a very important significance that classifies and recognizes the fault through timely finding faults and monitoring the state. It can also avoid unnecessary trouble and loss and provide a powerful guarantee for the victory of war. The work environment of automatic loader is very bad and the vibration is also obvious, which leads to the background noise in the signal is quite large. A part of the useful signals may be covered by strong noise. The traditional signal analysis methods may not reach the expected effect of the diagnosis. Here introduced current signal analysis, as MCSA. It uses induction motor stator current as a signal analysis the point, and studies the characteristics and the corresponding relation of failure. As a new method of fault diagnosis, it used in some other means inconvenience or easily found the problem the implementation of the occasion. This paper’s processing method is real-time monitoring the four sets of dc servo motor operation by hall current sensor. And it can also transfer the electrical signals to DASP signal collection system for post processing. In the later processing, it can classify all kinds of signal under the condition, and extract current signal characteristic parameters which are related. Finally it can use BP neural network to classify and diagnosis for fault.