Bayesian methodology for diagnosis uncertainty quantification and health monitoring

Data loss is a common problem of structural health monitoring and adversely affects many structural health monitoring applications. Tremendous progress in missing structural health monitoring data imputation has been made in recent years, forming an important part of sensor validation. Most of the imputed data are based on estimates obtained by data-driven statistical or machine learning models; quantifying their estimation uncertainties is significant in terms of being able to perform accuracy assessments and providing more insights into the imputed data. However, this procedure has been surprisingly neglected in the structural health monitoring community. This article focuses on uncertainty quantification for the distribution-to-warping function regression method (an indirect distribution-to-distribution regression method) used in reconstructing distributions of missing data. The distribution-to-warping function regression method belongs to the framework of functional data analysis as both the predictor and response are continuous functions. The challenge of performing uncertainty quantification for the distribution-to-warping function regression method comes not only from the functional nature of warping functions but also from their inherent constraints. To this end, a functional transformation is employed to transform warping functions into a vector space, and the confidence estimation for the regression operator is conducted in the vector space based on functional principal component analysis and bootstrapping. Then, the confidence region of the conditional expectation of missing distribution (caused by data loss) can be further estimated and visualized. In addition, a calibration processing procedure is also considered to obtain improved estimates of the confidence intervals with a better coverage accuracy under the desired probability. Simulation studies are conducted to validate and illustrate the proposed method, and then, it is applied to field strain monitoring data.

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Approach for Uncertainty Quantification And Management Of Unmanned Aerial Vehicle Health

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.847 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Matteo Corbetta ◽

Chetan S. Kulkarni

Keyword(s):

Uncertainty Quantification ◽

Health Monitoring ◽

Dominant Role ◽

Low Cost ◽

Dynamic Performance ◽

Autonomous Systems ◽

Health And Safety ◽

Formal Approach ◽

Uncertainty Sources ◽

Low Altitude

The increasing interest in low-altitude unmanned aerial vehi- cle (UAV) operations is bringing along safety concerns. Per- formance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is sig- nificantly lower when compared to reliability of commercial aircraft, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of differ- ent size, propulsion systems, dynamic performance and reli- ability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research tasks and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitor- ing, but uncertainty quantification is rarely addressed. Thus, this paper proposes a perspective towards uncertainty quan- tification for autonomous systems, giving special emphasis to UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is presented as case study. The example il- lustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.

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