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摘要:
针对运行态智能电表难以实现可靠寿命准确预估的问题，基于广义多应力加速模型，利用加速退化的试验数据研究并确定了智能电表的寿命分布规律，首先通过分析环境应力与Weibull分布模型参数的关系，建立了新的基于对数线性回归模型的多应力退化模型；之后提出了对该新模型的参数校正的方法，实现了正常应力水平下寿命分布模型参数的求解，获得了正常应力水平下智能电表的可靠寿命及其剩余寿命的预测结果；最后设置了正常应力条件，验证了该方法的可行性，为智能电表可靠寿命的综合评估提供了一种研究方法。
Abstract:Aimed at the difficulty to accurately predict the reliability life of smart meter in running state, based on the generalized multi-stress accelerated model, this study researched and determined the life distribution rules of smart meter by using the accelerated degradation test data. Through the analysis of the relationship between the environment stress and the parameters of Weibull distribution model, a new multi-stress degradation model based on log-linear regression model was established, parameters correction method of the new model was proposed, the solution of the parameters of life distribution model was realized, and the prediction consequence of the reliability life and remaining life of smart meter under normal stress level was obtained. The normal stress condition is set up at the end of the paper, and the feasibility of the method is verified, so that a research method is provided for the reliability life assessment of smart meter.
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Key words:
- smart meter /
- reliability life /
- log-linear regression model /
- life distribution /
- Weibull distribution
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图 1 不同应力条件下智能电表最小伪寿命分布概率图
Figure 1. Distribution probability map of minimum pseudo life of smart meter under different stress conditions
图 2 正常应力条件下智能电表可靠度变化趋势
Figure 2. Reliability change trend of smart meter under normal stress conditions
表 1 智能电表加速退化试验应力条件设置[6]
Table 1. Accelerated degradation test stress conditions setting for smart meter[6]
应力条件 T/℃ RH/% I/A S1 80 80 60 S2 55 80 40 S3 55 95 20 S4 70 95 60 S5 70 95 40 
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表 2 不同应力条件下智能电表敏感参数的最小伪寿命
Table 2. Minimum pseudo life of sensitivity parameters of smart meter under different stresses conditions
a S1 S2 S3 S4 S5 0.206 0.271 0.207 0.109 0.300 0.284 0.387 0.208 0.162 0.302 0.292 0.471 0.242 0.201 0.303 0.293 0.635 0.243 0.217 0.304 0.304 0.778 0.251 0.231 0.307 0.305 0.811 0.280 0.250 0.308 0.317 0.868 0.280 0.257 0.311 0.323 0.914 0.280 0.261 0.316 0.326 0.914 0.287 0.297 0.319 0.335 0.920 0.293 0.298 0.334 0.343 0.928 0.299 0.307 0.337 0.344 0.933 0.299 0.309 0.342 0.351 0.952 0.308 0.311 0.347 0.356 0.953 0.316 0.313 0.347 0.363 0.964 0.319 0.323 0.351 0.369 1.043 0.321 0.330 0.358 0.379 1.056 0.326 0.333 0.365 0.385 1.113 0.326 0.338 0.366 0.386 1.125 0.330 0.341 0.372 0.389 1.132 0.333 0.365 0.373 0.401 1.141 0.334 0.365 0.377 0.411 1.156 0.337 0.365 0.378 0.421 1.182 0.338 0.373 0.384 0.425 1.186 0.339 0.380 0.384 0.426 1.220 0.344 0.386 0.389 0.433 1.227 0.348 0.389 0.390 0.441 1.255 0.348 0.392 0.391 0.442 1.271 0.350 0.393 0.397 0.446 1.274 0.351 0.409 0.404 0.451 1.302 0.354 0.410 0.405 0.455 1.307 0.357 0.416 0.409 0.457 1.321 0.358 0.420 0.411 0.468 1.350 0.361 0.424 0.416 0.477 1.356 0.373 0.425 0.416 0.480 1.373 0.382 0.496 0.419 0.482 1.383 0.383 0.498 0.423 0.488 1.417 0.385 0.516 0.443 0.497 1.445 0.390 0.520 0.454 0.510 1.459 0.407 0.542 0.460 0.514 1.466 0.413 0.549 0.472 0.525 1.470 0.416 0.550 0.482 0.530 1.486 0.418 0.552 0.483 0.531 1.498 0.424 0.568 0.494 0.536 1.544 0.429 0.584 0.495 0.572 1.575 0.451 0.654 0.497 0.573 1.582 0.462 0.664 0.499 0.587 1.645 0.471 0.670 0.510 0.603 1.695 0.473 0.673 0.544 0.648 1.983 0.480 0.679 0.556 0.658 1.984 0.503 0.686 0.560 0.705 1.985 0.506 0.690 0.634 0.712 2.015 0.565 0.711 0.648 0.752 2.353 0.580 0.739 0.726 0.782 2.835 0.806 0.745 1.125
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表 3 不同应力条件下归一化相关系数
Table 3. Normalized correlation coefficient under different stress conditions
应力条件 相关系数 Weibull分布 正态分布 S1 0.973 0.981 S2 0.977 0.972 S3 0.897 0.859 S4 0.987 0.980 S5 0.866 0.847
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表 4 不同应力条件下最小伪寿命分布参数
Table 4. Distribution parameters of minimum pseudo life under different stress conditions
应力条件 β η S1 5.251 0.485 9 S2 2.523 1.444 9 S3 3.050 0.505 5 S4 3.311 0.549 1 S5 4.279 0.324 5
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表 5 正常应力条件下Weibull分布的参数值
Table 5. Parameter value of Weibull distribution under normal stress conditions
应力条件 β η S6_15 1.317 7 15.499 5 S7_15 1.081 2 21.413 1 S8_15 1.376 6 21.981 3 S6_17 0.338 7 162.200 0 S7_17 0.102 7 2 953.500 0 S8_17 0.395 3 411.200 0
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