.png){kind=icon}
返回
TBC-IG Random Variable-Dimension Algorithm for Aero-Engine Gas Path Sensor Optimization
.png){kind=icon}
OA
AI
DOI：10.3390/aerospace12110970.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The complex configuration of the internal flow field in aero-engines leads to limitations on sensor installation positions, and how to accurately identify the disturbances of the installation influence parameters under this constraint has long been a significant challenge. To address this issue, this study proposes an optimization algorithm to identify the optimal sensor layout. This is achieved by employing mutually distinct integer encoding, which ensures the uniqueness of each sensor position and prevents duplication. More importantly, an objective evaluation system incorporating tracking error, sensor comprehensiveness, and spatial coverage is integrated into the fitness function design, thereby overcoming the one-sidedness and limitations of single-indicator evaluation. Building upon this foundation, a sensor optimization scheme is proposed for identifying installation influence parameters. This scheme integrates the rapid search capability of the Tabu Bee Colony Random Variation Dimension Algorithm with the global optimization capability of an Improved Genetic Random Variation Dimension Algorithm, resulting in a Tabu Bee Colony-Improved Genetic Random Variation Dimension Optimization Algorithm (TBC-IG-RVDOA). For each installation influence parameter, different perturbation conditions were established, and the selected optimal sensor combination was then validated using the Extended Kalman Filter (EKF). Experimental studies show that, under all perturbation scenarios, the TBC-IG-RVDOA demonstrates strong convergence, high computational efficiency, and fitness function values consistently exceeding 0.92, thereby accurately capturing the changes in each installation influence parameter.
Keyword:
aero-engine
sensor optimization
tabu search
bee colony
improved genetic
random variable-dimension
期刊
IF:
2.2
论文数:
870
被引数:
7.3K
机构
引用论文
Bridge monitoring with satellite InSAR technology and clustering algorithm基于卫星InSAR技术和聚类算法的桥梁监测.png)
Measurement
OAAI
MeasurementIF5.6
OAAI分享收藏Extended least squares support vector machine with applications to fault diagnosis of aircraft engine扩展最小二乘支持向量机在航空发动机故障诊断中的应用ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏分享收藏Flexible piezoresistive sensing device with integrated design and fabrication for health monitoring集成设计与制备的柔性压阻传感装置用于健康监测
PREAI
分享收藏Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space改进蝙蝠算法在三维空间无人机航迹规划中的应用IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容