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The traditional sensor fault data analysis system hardware and program design are not compatible enough, and there is a problem that the real-time performance is poor and the analysis result is not accurate enough. Based on this, a new sensor fault data analysis system based on deep learning is proposed, which consists of sensor, ARM data processor, main circuit board, FODI data processor, integrated acquisition interface board, fault data sensor, multi-turn sensor, The field effect sensor and GKCL memory form the hardware structure of the system. The biggest feature of ASVH248 is the high resolution, which can effectively improve the clarity of the system display. Fault data acquisition programs, data processing programs, and data storage programs are designed separately. In order to check the effectiveness of the system, the internal data of the sensor is collected by the acquisition program, the processing program analyzes the data result, and the storage program is responsible for recording the result of the analysis. The contrast experiment was set up. The results show that the accuracy of the analysis results of the sensor fault data analysis system based on deep learning design is 15.28%, and the real-time performance is stronger and the use value is higher.