Technical Architecture and Application Value of Fault Early Warning System for Electron Beam High-Voltage Power Supply

The in-depth application of electron beam technology in industrial irradiation, semiconductor ion implantation, and material modification has made the stable operation of its core power source—the electron beam high-voltage power supply—a key factor in ensuring production continuity and process accuracy. This type of power supply typically has an output voltage ranging from tens to hundreds of kilovolts and must operate continuously in high-power, high-field environments. If faults such as insulation breakdown, module overheating, or excessive voltage fluctuation occur, it will not only cause production line shutdowns and huge economic losses but also may lead to equipment damage or even safety accidents. Therefore, building a fault early warning system with real-time performance and accuracy has become a core requirement for the intelligent upgrading of electron beam equipment.
The core architecture of the electron beam high-voltage power supply fault early warning system can be divided into three layers: the data acquisition layer, the edge computing layer, and the early warning decision-making layer. The data acquisition layer needs to overcome the challenge of signal isolation in high-voltage environments. Through customized high-voltage sampling modules, it captures key parameters during the power supply operation in real time, including the output voltage ripple coefficient, the temperature of the high-voltage module housing, the dielectric loss value of insulating oil, the electron beam current stability, and the flow rate of the cooling system. All parameters are transmitted via optical fibers to achieve complete electrical isolation between the high-voltage and low-voltage sides, avoiding data distortion caused by interference.
The edge computing layer is responsible for real-time data preprocessing. It eliminates transient pulse interference through sliding window filtering and normalizes multi-dimensional data, converting unstructured operational data into analyzable feature vectors. This ensures that the data processing delay is controlled at the millisecond level, meeting the real-time requirements of fault early warning for high-voltage power supplies.
The early warning decision-making layer is the core of the system, which uses a fusion algorithm to achieve accurate fault prediction. On one hand, it uses a Long Short-Term Memory (LSTM) network to conduct time-series analysis on historical operational data and real-time data, capturing trending anomalies in parameter changes—such as the gradual increase in voltage fluctuation amplitude over operating time—to identify potential module aging faults in advance. On the other hand, it combines Fault Tree Analysis (FTA) to build a logical model, establishing a mapping relationship between features (such as decreased insulation resistance and enhanced corona discharge signals) and specific fault types (such as aging of high-voltage bushings and loose internal circuit connections), enabling accurate localization of fault types. The system also features dynamic threshold adjustment, which can automatically optimize the early warning threshold according to changes in the power supply load under different process scenarios (e.g., electron beam current switching from 50mA to 100mA), avoiding false alarms or missed alarms caused by fixed thresholds.
The application of this fault early warning system has completely changed the traditional post-fault maintenance mode of electron beam high-voltage power supplies. In the field of industrial irradiation, the system can predict potential faults of high-voltage modules 72 hours in advance, reserving sufficient shutdown and maintenance time for maintenance personnel and avoiding batch scrapping of irradiated products due to insufficient dosage. In the semiconductor ion implantation process, real-time monitoring and early warning of power supply voltage stability can prevent chip doping concentration deviations caused by voltage fluctuations, improving product yield. From a long-term perspective, the system increases the Mean Time Between Failures (MTBF) of electron beam high-voltage power supplies by more than 40% through predictive maintenance, while reducing fault repair time by 50%. This significantly lowers the life-cycle operation and maintenance costs of equipment, providing reliable support for the large-scale application of electron beam technology in high-end manufacturing.