Construction and Application of Life Assessment Model for Electron Beam High-Voltage Power Supply

The large-scale application of electron beam technology in fields such as material surface modification, low-temperature food sterilization, and industrial non-destructive testing has raised higher reliability requirements for its core power component—the electron beam high-voltage power supply. This power supply needs to stably output high voltage ranging from tens to hundreds of kilovolts for a long time, while withstanding load current fluctuations and complex environmental stresses. Its service life directly determines the operation and maintenance costs of electron beam equipment and production continuity. The traditional replacement strategy based on fixed cycles often leads to over-maintenance or sudden failure. Therefore, establishing a scientific life assessment model for electron beam high-voltage power supplies has become a key direction for technological breakthroughs in the industry.
The core logic of the life assessment model revolves around the correlation between stress-failure-life and consists of three core links. Firstly, in the design of accelerated aging tests, it is necessary to simulate the actual working conditions of electron beam equipment, select high-voltage amplitude (±5% of rated value), load current fluctuation (0-120% of rated load), and ambient temperature (-10℃-45℃) as key stress factors. The step-stress method is used to shorten the aging cycle and obtain performance degradation data of the power supply under different stress combinations—such as increased output voltage ripple coefficient, decreased insulation resistance, and excessive partial discharge volume, which are precursor parameters of failure. Secondly, in the construction of the failure physics model, it is necessary to model the core failure mechanisms of the power supply: for the insulation system (such as epoxy resin encapsulation layer and polyimide insulation film), a partial discharge-dielectric loss correlation model is adopted to quantify the negative correlation between discharge volume and insulation life; for power devices (such as IGBTs and high-voltage thyristors), the rainflow counting method is used to count the number of junction temperature cycles, and the Coffin-Manson model is combined to calculate the thermal fatigue life; for filter capacitors (such as high-voltage aluminum electrolytic capacitors), the fitting formula between electrolyte loss rate and ripple current is used to derive the capacitor life proportion. Finally, for life prediction correction, interference factors such as ambient humidity (insulation life decreases by 30% when relative humidity > 60%) and dust accumulation (device life shortens by 20% when heat dissipation efficiency decreases by 15%) are introduced, and model parameters are calibrated through actual working condition data to reduce prediction errors.
The verification and application of the model rely on actual scenario data. In the test of an electron beam welding equipment, the predicted life of the power supply under accelerated stress (12,000 hours) was compared with the on-site continuous operation data (actual life of 11,800 hours), with an error controlled within 2%. In food sterilization equipment, the model warned of the aging risk of the insulation system of a power supply 600 hours in advance by real-time monitoring of output voltage stability and ambient temperature data, avoiding production interruption. In addition, the model can also reversely guide the optimization of power supply design—through life sensitivity analysis, it is found that improving the partial discharge resistance of insulation materials can increase the overall life of the power supply by 40%, providing a quantitative basis for research and development.
Compared with traditional empirical assessment methods, this model has the advantage of realizing the transformation from qualitative judgment to quantitative prediction: it not only covers the synergistic failure mechanisms of multiple internal components of the power supply but also takes into account the dynamic impact of external working conditions, providing an accurate basis for the operation and maintenance decisions of electron beam equipment. In the future, with the integration of the Internet of Things technology, the model can access real-time operation data of the power supply (such as online partial discharge monitoring and device temperature monitoring) to realize dynamic update and adaptive correction of life, further improving assessment accuracy and promoting the application expansion of electron beam technology in fields with high reliability requirements.