Diagnosability-optimized design of unmanned aerial vehicles based on structural analysis and maximum mean covariance differences

• Published in: Measurement : journal of the International Measurement Confederation Vol. 238; p. 115334
• Main Authors: Gu, XuPing, Shi, Xianjun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024
• Subjects: Diagnosability; Maximum mean covariance difference; Structural analysis; Tree seed algorithm; Unmanned aerial vehicle; Maximum mean covariance difference; Tree seed algorithm; Unmanned aerial vehicle; Diagnosability; Structural analysis
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2024.115334

•The MMCD-based method for the quantitative evaluation of diagnosability is proposed.•The TSA-based design strategy for diagnosability optimization is proposed.•The advantage of quantitative evaluation of diagnosability was obtained experimentally.•The diagnosability of UAVs is analyzed and optimized. This study presents a proposed strategy for optimizing diagnosability in unmanned aerial vehicles (UAVs) through a combination of Structural Analysis (SA) and Maximum Mean Covariance Difference (MMCD). Initially, diagnosability is qualitatively assessed using SA. Residuals are then designed based on the minimal structural overdetermination (MSO) using the sequential residual generation approach. Subsequently, diagnosability is reframed as a measurement of residual variance in the probability distribution. To address the absence of Maximum Mean Difference (MMD) in covariance difference measurement, Maximum Covariance Difference (MCD) is introduced. Furthermore, a method for quantitatively evaluating diagnosability based on MMCD is proposed, incorporating both MMD and MCD. Finally, an optimization model for diagnosability is established to ensure the efficient completion of fault diagnostic tasks with minimal cost and maximum effectiveness. The proposed method’s rationality and effectiveness are validated through simulation using the Tree Seed Algorithm (TSA) on a fixed-wing UAV model.