| 基于改进FUKF的燃气轮机性能退化诊断 |
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| 引用本文： | 曾力,龙伟,李炎炎.基于改进FUKF的燃气轮机性能退化诊断[J].西南交通大学学报,2018,53(4):873-878. |
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| 作者姓名： | 曾力 龙伟 李炎炎 |
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| 作者单位： | 四川大学空天科学与工程学院;四川大学制造科学与工程学院 |
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| 基金项目： | 国家绿色制造系统集成资助项目工信部节函[2017]327 |
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| 摘 要： | 针对燃气轮机使用过程中由于工作状态突变导致轮机性能估计困难的问题，提出基于残差相似性的渐消无迹卡尔曼滤波（fading unscented Kalman filter with residual similarity，FUKF-RS）算法，实现燃气轮机健康参数的估计.首先，在普通渐消无迹卡尔曼滤波（fading unscented Kalman filter，FUKF）框架下，构造燃气轮机健康参数估值算法，在测量值估计更新过程中，乘以渐消因子来调节前后时刻的权重，通过强制残差正交来估计渐消因子；然后，利用前后估值时刻残差向量的余弦值表征残差阵的相似度，根据其相似度的大小关系确定残差阵的比例；最后，用该比例值代替算法中的遗忘因子，计算残差阵，实现求解的量化取值.研究结果表明:在燃气轮机状态突变条件下，FUKF-RS算法具有突变状态跟踪能力，参数估值精度比FUKF算法提高了3%左右，普通UKF（unscented Kalman filter，UKF）则不具突变状态跟踪能力；在部件性能缓慢变化时，参数的估计曲线比普通FUKF更平滑，估计精度提高了2%左右.
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| 关 键 词： | 燃气轮机诊断 状态突变 无迹卡尔曼滤波 残差约束 残差相似性 |
| 收稿时间： | 2016-07-10 |
Performance Degradation Diagnosis of Das Turbine Based on Improved FUKF |
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| ZENG Li,LONG Wei,LI Yanyan.Performance Degradation Diagnosis of Das Turbine Based on Improved FUKF[J].Journal of Southwest Jiaotong University,2018,53(4):873-878. |
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| Authors: | ZENG Li LONG Wei LI Yanyan |
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| Abstract: | Owing to the difficulty in assessing the performance of a gas turbine during usage, because of sudden changes in its operation state, the use of an fading unscented Kalman filter with residual similarity (FUKF-RS) algorithm for the health parameter estimation of a gas turbine is proposed in this study. At first, under the common fading unscented Kalman filter (FUKF) framework, the health parameter estimation algorithm of the gas turbine was built. The weights before and after estimation were adjusted by multiplying the fading factor with the variance of measured value during the updating process of estimation; the fading factor was estimated by keeping the residual vectors to be orthogonal. Then, the similarity of the residual matrix was represented by the cosine value of the residual vector before and after the estimation, and the proportion of residual matrixes was determined according to the magnitude of the similarity. Finally, the fading factor of the algorithm was substituted by such proportion to calculate the residual matrix and obtain the quantitative parameter required for the calculation. The results show that the FUKF-RS algorithm can trace the sudden change of state of the gas turbine, and its accuracy in parameter estimation is higher by approximately 3% as compared to that of the FUKF algorithm. Additionally, as the component performance changes slowly, the parameter estimation curve will be smoother than that of the common FUKF, and the estimation accuracy will be increased by approximately 2%. |
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