| 孙宏达,景博,黄以锋,李龙腾,陈鹏宇.基于人工免疫克隆选择算法的不可靠测试点优化[J].电子测量与仪器学报,2021,35(2):152-160 |
| 基于人工免疫克隆选择算法的不可靠测试点优化 |
| Optimization of unreliable test points based on artificial immune clone selection algorithm |
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| DOI： |
| 中文关键词: 不可靠测试 测试点优化 人工免疫算法 克隆选择 |
| 英文关键词:unreliable test test point optimization artificial immune algorithm clone selection |
| 基金项目:“十三五”装备预先研究共同技术公开项目（41402010102）资助 |
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| Author | Institution |
| Sun Hongda | 1. College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China; |
| Jing Bo | 1. College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China; |
| Huang Yifeng | 1. College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China; |
| Li Longteng | 1. College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China; |
| Chen Pengyu | 2. Sixth Military Representative Office in Chengdu, Chengdu 610000, China |
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| 摘要点击次数: 524 |
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| 中文摘要: |
| 针对实际复杂系统诊断与测试过程中普遍存在的不确定性问题，提出测试不可靠条件下基于人工免疫克隆选择算法（artificial immune clone selection algorithm, AICS）的测试点优化选择方法。通过综合考虑故障检测率、隔离率、虚警率以及测试总费用等性能指标，构造了反映测试点集性能的适应度函数，并设计了基于AICS的不可靠测试点优化方案，有效地降低了算法复杂度，时间开销缩减到0496 s，提高了运行效率。最后用燃油耗量测量系统的耗量组件进行实例验证，结果表明该方法能够获得在满足故障检测率、隔离率、虚警率等性能指标要求下,使得测试总费用最少的测试点集合，并且其综合性能指标优于遗传算法和模拟退火粒子群算法。 |
| 英文摘要: |
| Aiming at the common uncertainty in the diagnosis and testing of practical complex systems, an optimal selection method of test points based on artificial immune clone selection algorithm (AICS) is proposed under the unreliable condition. In this model, a fitness function reflecting the performance of the test points is constructed by comprehensively considering the performance indexes such as fault detection rate, isolation rate, false alarm rate and total test cost, and an unreliable test point optimization scheme is designed based on AICS, which effectively reduces the complexity computing. As a result, the time cost is reduced to 0496 seconds, which demonstrates the improvement efficiency of proposed model. Finally, this model is verified by a test utilized with the consumption component in the fuel consumption measurement system. The results show that this method can obtain a set of test points with the lowest test cost while meeting the performance requirements of fault detection rate, isolation rate, false alarm rate, and its comprehensive performance index is better than that of genetic algorithm and simulated annealing particle swarm optimization algorithm. |
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