Performance Degradation Diagnosis of Das Turbine Based on Improved FUKF
-
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.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%.
-
Key words:
- diagnosis of gas turbine /
- state mutation /
- UKF /
- residual constraint /
- residual similarity
-
图 1 基于UKF的流量系数突变时的估计值
Figure 1. Estimation of the discharge coefficientduring state mutation based on UKF
图 2 基于FUKF估计高压压气机的健康参数
Figure 2. Estimation of the health parameter of highpressure compressor based on FUKF
图 3 基于FUKF-RS估计高压压气机的健康参数
Figure 3. Estimation of the health parameter of high pressure compressor based on FUKF-RS
图 4 基于FUKF-RS的高压涡轮流量系数突变时的估计值
Figure 4. Estimation of the discharge coefficient of high pressure turbine during state mutation based on FUKF-RS
-
胡宇, 张世英, 杨月诚, 等.基于超球体平方根无迹Kalman滤波算法的涡扇发动机气路部件故障诊断[J].航空动力学报, 2014, 29(3):690-695. http://d.old.wanfangdata.com.cn/Periodical/hkdlxb201403029HU Yu, ZHANG Shiying, YANG Yuecheng, et al. Fault diagnosis of gas path components of turbofan engine based on spherical square root unscented Kalman filter algorithm[J]. Journal of Aerospace Power, 2014, 29(3):690-695. http://d.old.wanfangdata.com.cn/Periodical/hkdlxb201403029 黄辉先, 任科明, 李燕, 等.基于渐消卡尔曼滤波算法的航空发动机参数估计办法[J].计算机应用, 2013, 33(10):2993-2995. http://d.wanfangdata.com.cn/Periodical/kxjsygc201333034HUANG Huixian, REN Keming, LI Yan, et, al. Aero-engine parameters estimation using fading Kalman filter algorithm[J]. Journal of Computer Applications, 2013, 33(10):2993-2995. http://d.wanfangdata.com.cn/Periodical/kxjsygc201333034 王小旭, 赵琳, 夏全喜, 等.基于Unscented变换的强跟踪滤波器[J].控制与决策, 2010, 25(7):1064-1068. http://d.old.wanfangdata.com.cn/Periodical/kzyjc201007020WANG Xiaoxu, ZHAO Lin, XIA Quanxi, et al. Strong tracking filter based on unscented transformation[J]. Control and Decision. 2010, 25(7):1064-1068. http://d.old.wanfangdata.com.cn/Periodical/kzyjc201007020 SEBASTIEN B, OLIVIER L. Coupling principal component analysis and kalman filtering algorithm for on-line aircraft engine diagnosis[J]. Control Engineering Practice. 2009, 17(4):494-502. doi: 10.1016/j.conengprac.2008.09.008 张鹏.基于卡尔曼滤波的航空发动机故障诊断技术研究[D].南京: 南京航空航天大学, 2009. http://cdmd.cnki.com.cn/Article/CDMD-10287-2010079957.htm 李冬, 李本威, 赵鹏飞, 等.基于卡尔曼滤波和主元分析的发动机性能衰退[J].海军航空工程学院学报, 2013, 28(2):127-131. doi: 10.3969/j.issn.1673-1522.2013.02.009LI Dong, LI Benwei, ZHAO Pengfei, et al. Engine performance deterioration based on kalman filter and principle component analysis[J]. Journal of Naval Aeronautical and Astronautical, 2013, 28(2):127-131. doi: 10.3969/j.issn.1673-1522.2013.02.009 胡宇, 杨月诚, 张世英, 等.基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控[J].航空动力学报, 2014, 29(2):442-450. http://d.old.wanfangdata.com.cn/Periodical/hkdlxb201402025HU Yu, YANG Yuecheng, ZHANG Shiying, et al. Turbofan engine gas path performance monitoring based on improved square root unscented Kalman filter[J]. Journal of Aerospace Power, 2014, 29(2):442-450. http://d.old.wanfangdata.com.cn/Periodical/hkdlxb201402025 SIMON D, GARG S. A systematic approach for model-based aircraft engine performance estimation[C]//AIAA Infotoch@aeropaceConference, 2009. Seattle: AIAA, 2009: 1-18. RAJAMANI R, WANG J, JEONG K Y. Conditioned-based maintenance for aircraft engine[C]//ASME Turbo Expo 2004: Power for Land, Sea, and Air. New York: American Society of Mechanical Engineers, 2004: 819-823. 刘铮. UKF算法及其改进算法的研究[D].长沙: 中南大学, 2009. 刘贵喜, 马涛, 陈石磊.应用最小偏度采样的UPF算法[J].光学精密工程, 2008, 16(4):746-750. doi: 10.3321/j.issn:1004-924X.2008.04.028LIU Guixi, MA Tao, CHEN Shilei. Unscented particle filtering algorithm using minimal skew sampling[J]. Optics and Precision Engineering, 2008, 16(4):746-750. doi: 10.3321/j.issn:1004-924X.2008.04.028 崔波, 张家树, 杨宇.基于分块EnKF的非线性目标跟踪算法[J].西南交通大学学报, 2013, 48(5):864-869. http://manu19.magtech.com.cn/Jweb_xnjd/CN/abstract/abstract11837.shtmlCUI Bo, ZHANG Jiashu, YANG Yu. Nonlinear target tracking algorithm based on block ensemble kalman filter[J]. Journal of Southwest Jiaotong University, 2013, 48(5):864-869. http://manu19.magtech.com.cn/Jweb_xnjd/CN/abstract/abstract11837.shtml 张祖涛, 张家树.基于UKF非线性人眼跟踪的驾驶员疲劳检测[J].西南交通大学学报, 2008, 43(6):698-702. http://manu19.magtech.com.cn/Jweb_xnjd/CN/abstract/abstract8962.shtmlZHANG Zutao, ZHANG Jiashu. Driver fatigue detection based on unscented kalman filter and eye tracking[J]. Journal of Southwest Jiaotong University. 2008, 43(6):98-702. http://manu19.magtech.com.cn/Jweb_xnjd/CN/abstract/abstract8962.shtml -
下载:
点击查看大图
图(5)
计量
- 文章访问数: 449
- HTML全文浏览量: 140
- PDF下载量: 71
- 被引次数: 0