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Method for determining thermal-mechanical coupling ignition threshold value of energetic material

PendingCN121955080AComprehensive assessmentThe test results are accurate and reliableMaterial exposibilityComplex mathematical operationsProcess engineeringShock sensitivity
The invention belongs to the technical field of safety of energetic materials, and particularly discloses a method for determining a thermal-mechanical coupling ignition threshold value of an energetic material, which comprises the following steps: firstly, heating an energetic material sample to a preset specific temperature, simulating a possible thermal environment of the energetic material sample, and then immediately carrying out an impact sensitivity test at the temperature to determine the thermal-mechanical coupling ignition threshold value of the energetic material. And thus, a safety threshold closer to an actual risk scene is obtained. According to the determination method, the safety threshold closer to the actual risk scene can be obtained, and the method has the characteristics of comprehensive evaluation and accurate and reliable test result.
Owner:BEIJING INST OF TECH

A Rotary Equipment Fault Diagnosis System Based on a Large Language Model

This invention discloses a fault diagnosis system for rotating equipment based on a large language model, aiming to improve the accuracy and efficiency of fault diagnosis for industrial rotating equipment. The system consists of six parts: data collection, data preprocessing, a neural network model, a time-series label knowledge base, a large language model, and a user unit. The system can not only detect faults in real time but also enable interaction through the user unit, allowing for analysis and prediction of the rotating equipment's operating status based on user needs. The system optimizes input through data preprocessing techniques and enhances the large language model using a time-series label knowledge base. Based on this, and combining equipment operating data, historical records, and neural network diagnostic results, the large language model can accurately identify potential faults and generate actionable maintenance suggestions, thereby significantly improving equipment reliability and the safety of industrial production.
Owner:CHINA YANGTZE POWER