A Study on the Algorithm of Fault Information Automatic Detection for High-Precision Intelligent Instruments

Abstract

Due to the complex structure, high frequency of occurrence of the fault signal and the short cycle, it is difficult to form long lasting identifiable characteristics for intelligent instruments. The traditional fault detected model is difficult to effectively extract short fault signal, and unable to form meaningful fault recognition model, resulting in difficulties in the detection. In order to solve these problems, this paper presents a mining method for fault symptoms of the intelligent instruments based on the neural network model. The method uses the fault characteristic of the intelligent instrument as its basic data, according which to build neural network model in order to realize fault mining. The experiment results show that the proposed method can instantly detect the failure of the intelligent instruments, according to which the treatment can be carried out rapidly and accurately to ensure the performance of the instruments.