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.

scholarly journals A framework for data compression and damage detection in structural health monitoring applied on a laboratory three-story structure

2016 ◽  
Vol 8 (2) ◽  
pp. 129
Author(s):  
Manoel Afonso Pereira de Lima ◽  
Claudomiro Sales ◽  
Adam Santos ◽  
Reginaldo Santos ◽  
Moisés Silva ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Data Compression ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Strong Impact ◽  
Story Structure ◽  
Compression Algorithms ◽  
Structural Health

Structural Health Monitoring (SHM) is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.

Strukturüberwachung von Straßenbrücken durch Bauwerksmonitoring – Teil 2: Anomalieerkennung mittels Hauptkomponentenanalyse/Structural health monitoring of road bridges – Part 2: Anomaly detection with principal component analysis

Bauingenieur ◽  
2021 ◽  
Vol 96 (10) ◽  
pp. 349-357
Author(s):  
Andreas Jansen ◽  
Karsten Geißler
Keyword(s):  
Principal Component Analysis ◽  
Structural Health Monitoring ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Structural Health

Die messtechnische Strukturüberwachung von Brücken hat das Potenzial, sich langfristig als wichtiges ergänzendes Instrument zur kontinuierlichen Zustandsbewertung zu etablieren. Die jüngere Forschung auf diesem Gebiet setzt verstärkt auf Signalmerkmale unterschiedlicher Sensortypen sowie auf Methoden des maschinellen Lernens. Daran anknüpfend wird im Teil 2 dieses Aufsatzes erläutert, wie Bauwerksschäden mithilfe der Anomalieerkennung mit Modellen des maschinellen Lernens identifiziert werden können. Im Teil 1 wurde dazu ein Signalmerkmal vorgestellt, das auf Einflusslinien basiert: die R-Signatur. Durch Simulationen kann gezeigt werden, dass die R-Signatur deutlich empfindlicher auf einen Bauwerksschaden reagiert als die betrachteten Eigenfrequenzen. Im Teil 2 wird ein Verfahren zur Anomalieerkennung beschrieben, das Bauwerksschäden durch eine Veränderung der Korrelationsstruktur der R-Signatur identifiziert. Das zugrunde liegende Datenmodell nutzt dabei die Hauptkomponentenanalyse. Der vorgestellte Ansatz wurde mit den Messdaten einer Straßenbrücke verifiziert.

Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

2021 ◽  
pp. 147592172110355
Author(s):  
Kang Yang ◽  
Sungwon Kim ◽  
Rongting Yue ◽  
Haotian Yue ◽  
Joel B. Harley
Keyword(s):  
Principal Component Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Guided Wave ◽  
Mitigation Strategies ◽  
Reconstruction Method ◽  
Short Term ◽  
Structural Health

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

2017 ◽  
Vol 17 (4) ◽  
pp. 727-746 ◽  
Author(s):  
Seung-Seop Jin ◽  
Hyung-Jo Jung
Keyword(s):  
Principal Component Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Normal Condition ◽  
Environmental Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Baseline Model ◽  
Structural Health ◽  
Natural Change

Damage-sensitive features such as natural frequencies are widely used for structural health monitoring; however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde–Buzo–Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, the proposed method is the only successful method to deal with the non-linear dependency among features and detect the structural damage timely.

Vibration-Based Structural Health Monitoring of a Simulated Beam with a Breathing Crack

2013 ◽  
Vol 569-570 ◽  
pp. 1093-1100 ◽  
Author(s):  
Jyrki Kullaa ◽  
Kari Santaoja ◽  
Anthony Eymery
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Crack Length ◽  
Health Monitoring ◽  
Early Stage ◽  
Novelty Detection ◽  
Principal Component ◽  
Extreme Value Statistics ◽  
Ratio Test ◽  
Structural Health

Cracking is a common type of failure in machines and structures. Cracks must be detected at an early stage before catastrophic failure. In structural health monitoring, changes in the vibration characteristics of the structure can be utilized in damage detection. A fatigue crack with alternating contact and non-contact phases results in a non-linear behaviour. This type of damage was simulated with a finite element model of a simply supported beam. The structure was monitored with a sensor array measuring transverse accelerations under random excitation. The objective was to determine the smallest crack length that can be detected. The effect of the sensor locations was also studied. Damage detection was performed using the generalized likelihood ratio test (GLRT) in time domain followed by principal component analysis (PCA). Extreme value statistics (EVS) were used for novelty detection. It was found that a crack in the bottom of the midspan could be detected once the crack length exceeded 10% of the beam height. The crack was correctly localized using the monitoring data.

scholarly journals Magnetostrictive Sensors for Composite Damage Detection and Wireless Structural Health Monitoring

2019 ◽  
Vol 55 (7) ◽  
pp. 1-6
Author(s):  
Zhaoyuan Leong ◽  
William Holmes ◽  
James Clarke ◽  
Akshay Padki ◽  
Simon Hayes ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Health ◽  
Composite Damage
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