Decision fusion methodologies in Structural Health Monitoring systems

Mikhail, Maged; Zein-Sabatto, Saleh; Bodruzzaman, M.

doi:10.1109/secon.2012.6197066

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Due to the complexity induced by considering multi-type data sources, powerful mathematical tools such as machine or deep learning are attractive for feature-level fusion, as discussed in the previous paragraph on the consideration of data heterogeneity. Similarly, the complexity of designing decision-level fusion approaches may increase through the presence of additional and potentially conflicting data sources, and can be mitigated through the use of intelligent fusion algorithms, for example, based on fuzzy logic, adaptive neuro-fuzzy inference, or Dempster-Shafer theory, as shown by Mikhail et al [86] and Sun et al [87].…”

Section: Design Of Fusion Processmentioning

confidence: 99%

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

2022

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With the increased use of composites in aircraft, many new successful contributions to the advancement of the structural health monitoring (SHM) field for composite aerospace structures have been achieved. Yet its application is still not often seen in operational conditions in the aircraft industry, mostly due to a gap between research focus and application, which constraints the shift towards improved aircraft maintenance strategies such as condition-based maintenance (CBM). In this work, we identify and highlight two key facets involved in the maturing of the SHM field for composite aircraft structures: (1) the aircraft maintenance engineer who requires a holistic damage assessment for the aircraft’s structural health management, and (2) the upscaling of the SHM application to realistic composite aircraft structures under in-service conditions. Multi-sensor data fusion concepts can aid in addressing these aspects and we formulate its benefits, opportunities, and challenges. Additionally, for demonstration purposes, we show a conceptual design study for a fusion-based SHM system for multi-level damage monitoring of a representative composite aircraft wing structure. In this manner, we present how multi-sensor data fusion concepts can be of benefit to the community in advancing the field of SHM for composite aircraft structures towards an operational CBM application in the aircraft industry.

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Section: Design Of Fusion Processmentioning

confidence: 99%

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

2022

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“…Chen & Varshney 9 have proposed a decision fusion using hierarchical Bayesian model, which uses Gibbs sampler method to obtain posterior likelihood of an observation to make the final decision. Mikhail et al 10 have a done a good review of popularly used decision fusion methodologies in SHM. majority voting rule, fuzzy Logic method, Dempster's combination 11 rule among others are popularly used decision fusion method.…”

Section: Relevant Literature On Decision Fusion In Shmmentioning

confidence: 99%

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

et al. 2016

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In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with highdimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

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