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GIS is prone to various defects and faults after putting into operation, and the failure rate is high. At present, the inspection methods of GIS in operation include traditional inspection mode, two-dimensional code inspection mode, radio frequency identification technology mode and robot inspection mode. However, the current inspection robot has the disadvantages of weak adaptability to complex environment, insufficient inspection scope, low efficiency and low intelligence. Therefore, this paper designs a GIS monitoring system based on the rail robot. By installing all kinds of sensors on the rail robot, with the help of binocular vision and mechanical arm, the omni-directional live detection of GIS in transit can be realized.

Compared with the traditional reactor abnormal state detection method, the acoustic vibration detection method combines the advantages of acoustic detection and vibration detection, which has the advantages of simple installation, strong portability and good signal integrity, and has a broad application prospect. In this paper, the characteristic parameters of reactor acoustic vibration, including sound pressure level, sound power level, acoustic signal spectrum, vibration amplitude, vibration acceleration value, vibration signal spectrum, harmonic proportion and frequency complexity, are obtained to analyze the range and distribution rule of each characteristic parameter under long-term operation condition of reactor, and the weight factor of each characteristic parameter is determined by analytic hierarchy process On the basis of fuzzy reasoning, the state evaluation model of reactor is established.

Although the state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network. In this paper, the deep learning neural network model is constructed, and the data obtained from reactor defect simulation experiment and field measurement are used as samples to train the deep learning network. Through the training of neural network, the characteristics of acoustic vibration signal are automatically learned, and the characteristics are stored in the parameters of neural network. Finally, the state of reactor is realized by the classifier at the end of the network assessment