Data Normalization in Structural Health Monitoring by Means of Nonlinear Filtering

2019 ◽  
Author(s):  
Caio Flexa ◽  
Walisson Gomes ◽  
Claudomiro Sales
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Nonlinear Filtering ◽  
Data Normalization ◽  
Structural Health

Effects of environmental and operational variability on structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 539-560 ◽  
Author(s):  
Hoon Sohn
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Vibration Signal ◽  
Data Normalization ◽  
Operational Conditions ◽  
Structural Health ◽  
Structural Deterioration ◽  
Damage Data ◽  
Environmental Variations

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

Deep principal component analysis: An enhanced approach for structural damage identification

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1444-1463 ◽  
Author(s):  
Moisés Silva ◽  
Adam Santos ◽  
Reginaldo Santos ◽  
Eloi Figueiredo ◽  
Claudomiro Sales ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Structural Health Monitoring ◽  
Environmental Factors ◽  
Damage Detection ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Data Normalization ◽  
Structural Health ◽  
Detection And Quantification

The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal component analysis is probably the most studied algorithm in structural health monitoring, having numerous versions to learn strong nonlinear normal changes. However, in most cases, not all variability is properly accounted for via the existing nonlinear principal component analysis approaches, resulting in poor damage detection and quantification performances. In this article, a new paradigm based on deep principal component analysis, rooted in the deep learning field, is presented to overcome these limitations. This approach extracts the most salient underlying feature distributions by stacking multiple feedforward neural networks trained to learn an identity mapping of the input variables, where the network inputs are reproduced into the outputs. Similar to the traditional nonlinear principal component analysis–based approach, our approach identifies a nonlinear output-only model of an undamaged structure by comprising modal features into an internal bottleneck layer, which implicitly represents the independent environmental factors. The proposed technique is validated through the application on a progressively damaged prestressed concrete bridge and a three-span suspension bridge. The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances.

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