The paper aims to study the identification and diagnosis of infrared thermal fault of airborne circuit board of equipment, expand the application of intelligent algorithm in infrared thermal fault diagnosis, and promote the development of computer image processing technology and neural network technology in the field of thermal diagnosis. Taking the airborne circuit board in the boiler plant as the research object, first, the sequential analysis method was selected to collect the temperature changes during the operation of the circuit board. Second, on the basis of convolutional neural network, the program was written in Python, and the Relu function was used as the activation function establish the thermal fault diagnosis method of the on-board circuit board of the boiler plant equipment based on the convolutional neural network model. Third, based on the support vector machine intelligent algorithm, genetic algorithm was used to optimize the parameters, and combined with the grey prediction model, the infrared thermal fault diagnosis scheme of the circuit board of the multistage support vector machine boiler plant equipment was constructed. The results showed that the accuracy of the model after 6000 iterations was stable between 0.92-0.96, and the loss function value was stable at about 0.17. After the optimization of genetic algorithm, the accuracy of thermal fault diagnosis based on support vector machine model was optimized. Compared with grey prediction model, the accuracy of support vector machine model for fault diagnosis was higher, mean square error value was 0.0258, and the correlation coefficient was 91.55%. To sum up, the support vector machin model shows higher accuracy than grey prediction model, which can be used for thermal fault diagnosis.

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This project is a research of wind speed measurement system, with a high -precision, low-power, whose

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