Inicio  /  Aerospace  /  Vol: 10 Par: 3 (2023)  /  Artículo
ARTÍCULO
TITULO

Uncertainty Quantification of Imperfect Diagnostics

Vladimir Ulansky and Ahmed Raza    

Resumen

The operable state of a system is maintained during operation, which requires knowledge of the system?s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of any system. The study deals with the relevant problem of uncertainty quantification of imperfect diagnostics. We considered the most general case when the object of diagnostics, the diagnostic tool, and the human operator can each be in one of the many states. The concept of a diagnostic error is introduced, in which the object of diagnostics is in one of many states but is erroneously identified as being in any other state. We derived the generalized formulas for the probability of a diagnostic error, the probability of correct diagnosis, and the total probability of a diagnostic error. The proposed generalized formulas make it possible to determine the probabilistic indicators of diagnosis uncertainty for any structures of diagnostics systems and any types of failures of the diagnostic tool and human operator. We demonstrated the theoretical material by computing the probabilistic indicators of diagnosis uncertainty for an aircraft VHF communication system and fatigue cracks in the aircraft wings.

 Artículos similares

       
 
Haohao Wang, Limin Gao and Baohai Wu    
Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the amounts of data collected as to compressor blad... ver más
Revista: Aerospace

 
Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu    
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DN... ver más
Revista: Algorithms

 
Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein    
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi... ver más
Revista: Information

 
Vishnupriya Jonnalagadda, Ji Yun Lee, Jie Zhao and Seyed Hooman Ghasemi    
The nation?s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic ... ver más
Revista: Infrastructures

 
Mingzhi Li, Xianjun Yu, Dejun Meng, Guangfeng An and Baojie Liu    
Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eigenmo... ver más
Revista: Aerospace