欠压保护器产品电气性能与机械寿命预测数据
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基于采集的欠压保护器（本系列产品的所有型号为唯一性产品，所有型号均在市场上有公开销售）运行数据，包括电压偏差率、绝缘电阻衰减率、触点压力、操作频率、环境温度等核心参数，我们建立了多维度的性能评估体系。通过对电气寿命、机械寿命等关键指标的量化分析，为电力系统的预防性维护提供了数据支撑。具有以下内部应用场景：1.通过实时监测电压波动、接触阻抗特征，预测触点材料劣化趋势，优化检修周期。2.利用机械寿命测试数据，改进电磁机构材料工艺。3.产品寿命的最小值取法（电气与机械寿命的最小值）是确保欠压保护器在最薄弱环节仍能可靠工作的关键机制，对用户的经济性、安全性和系统效率均有显著贡献。还具有以下外部应用场景：1.通过接入电力监测系统实时数据流，该算法可生成区域电网设备寿命热力图，辅助运维人员优先更换寿命短节点设备。2.将现场采集的F/f参数异常数据反向传输至设计端，指导下一代产品电磁机构力矩优化。基于退化物理模型，通过实时监测电压波动、接触阻抗等参数，根据威布尔分布失效概率建立电气性能寿命公式：Le=A*{e（-B*（ΔV^C））}/ln（D*R+1），其中e在数学中是自然对数的底数，ln在数学中是以e为底的自然对数，Le为产品的电气性能寿命，Le取整数值，ΔV为电压偏差率（偏差率 = |实测电压-理论电压|/理论电压），R为绝缘电阻衰减率。再基于Paris疲劳裂纹扩展定律，结合触点压力、操作频率等数据，构建非线性磨损累积公式：Lm=α*（F^β）/{γ*f*（T^k）}，其中Lm为产品的机械寿命，Lm取整数值，F为触点压力，f为操作频率，T为环境温度。另外权重系数A、B、C、D、α、β、γ、k是通过历史监测数据（如操作次数、电压偏差记录）进行分布检验（正态性检验），再利用极大似然估计法求解系数最优值。最终该产品的寿命L=MIN（Le，Lm），MIN函数为直接比较两个数值，返回较小值。
Based on the collected operating data of undervoltage protectors (all models in this product series are unique and commercially available in the market), including core parameters such as voltage deviation rate, insulation resistance decay rate, contact pressure, operating frequency, and ambient temperature, we established a multi-dimensional performance evaluation system. Through quantitative analysis of key indicators such as electrical life and mechanical life, data support is provided for the preventive maintenance of power systems. It has the following internal application scenarios: 1. Predict the deterioration trend of contact materials and optimize the maintenance cycle by real-time monitoring of voltage fluctuations and contact impedance characteristics. 2. Improve the material processing technology of electromagnetic mechanisms using mechanical life test data. 3. The minimum value selection method for product life (the minimum of electrical and mechanical life) is a key mechanism to ensure that undervoltage protectors can still operate reliably at their weakest links, which makes significant contributions to user economic efficiency, safety, and system efficiency. It also has the following external application scenarios: 1. By accessing the real-time data stream of the power monitoring system, this algorithm can generate a heat map of the service life of regional power grid equipment, assisting operation and maintenance personnel to prioritize replacing equipment with short service life at each node. 2. The abnormal F/f parameter data collected on-site is transmitted back to the design end to guide the torque optimization of the electromagnetic mechanism of next-generation products. Based on the degradation physics model, by real-time monitoring of parameters such as voltage fluctuations and contact impedance, the electrical performance life formula is established according to the failure probability of Weibull distribution: $L_e = A cdot e^{(-B cdot (Delta V^C))} / ln(D cdot R + 1)$ where e is the base of natural logarithm in mathematics, ln is the natural logarithm with base e, $L_e$ is the electrical performance life of the product, and $L_e$ is an integer value; $Delta V$ is the voltage deviation rate (deviation rate = |measured voltage - theoretical voltage| / theoretical voltage), and $R$ is the insulation resistance decay rate. Furthermore, based on Paris' fatigue crack growth law and combined with data such as contact pressure and operating frequency, a nonlinear wear accumulation formula is constructed: $L_m = alpha cdot (F^eta) / (gamma cdot f cdot (T^k))$ where $L_m$ is the mechanical life of the product, and $L_m$ is an integer value; $F$ is the contact pressure, $f$ is the operating frequency, and $T$ is the ambient temperature. In addition, the weight coefficients A, B, C, D, α, β, γ, k are obtained by performing distribution tests (normality tests) on historical monitoring data (such as operating times and voltage deviation records), and then solving the optimal values of the coefficients using the maximum likelihood estimation method. Finally, the service life of the product is $L = MIN(L_e, L_m)$, where the MIN function directly compares two values and returns the smaller one.
