A damage classification approach for structural health monitoring using machine learning

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.Peer ReviewedPostprint (published version

Principal component analysis and self-organizing maps for damage detection and classification under temperature variations

The use of statistical techniques for data driven has proven very useful in multivariable analysis as a pattern recognition approach. Among their multiple advantages such as data reduction, multivariable analysis and the definition of statistical models built with data from experimental trials, they provide robustness and allow avoiding the need of the development of physical models which sometimes are difficult for modelling especially when the system is complex. In this paper, a methodology based on Principal Component Analysis (PCA) is developed and used for building statistical baseline models comprising the dynamics from the monitored healthystructureunderdifferenttemperatureconditions.Inasecondstep, fortesting the proposed methodology, data from the structure at different structural states and under different temperature conditions are projected into the baseline models in order to obtain statistical measures (Scores and Q-index) which are included as feature vectors in a Self-Organizing Map for the damage detection and classification tasks. The methodology is evaluated using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is present in both structures.Peer ReviewedPostprint (author’s final draft

Structural health monitoring by means of strain field pattern recognition on the basis of PCA and automatic clustering techniques based on SOM

Peer ReviewedPostprint (published version

Principal component analysis and self-organizing maps for damage detection and classification under temperature variations

The use of statistical techniques for data driven has proven very useful in multivariable analysis as a pattern recognition approach. Among their multiple advantages such as data reduction, multivariable analysis and the definition of statistical models built with data from experimental trials, they provide robustness and allow avoiding the need of the development of physical models which sometimes are difficult for modelling especially when the system is complex. In this paper, a methodology based on Principal Component Analysis (PCA) is developed and used for building statistical baseline models comprising the dynamics from the monitored healthystructureunderdifferenttemperatureconditions.Inasecondstep, fortesting the proposed methodology, data from the structure at different structural states and under different temperature conditions are projected into the baseline models in order to obtain statistical measures (Scores and Q-index) which are included as feature vectors in a Self-Organizing Map for the damage detection and classification tasks. The methodology is evaluated using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is present in both structures.Peer Reviewe

Principal component analysis and self-organizing maps for damage detection and classification under temperature variations

The use of statistical techniques for data driven has proven very useful in multivariable analysis as a pattern recognition approach. Among their multiple advantages such as data reduction, multivariable analysis and the definition of statistical models built with data from experimental trials, they provide robustness and allow avoiding the need of the development of physical models which sometimes are difficult for modelling especially when the system is complex. In this paper, a methodology based on Principal Component Analysis (PCA) is developed and used for building statistical baseline models comprising the dynamics from the monitored healthystructureunderdifferenttemperatureconditions.Inasecondstep, fortesting the proposed methodology, data from the structure at different structural states and under different temperature conditions are projected into the baseline models in order to obtain statistical measures (Scores and Q-index) which are included as feature vectors in a Self-Organizing Map for the damage detection and classification tasks. The methodology is evaluated using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is present in both structures.Peer Reviewe

Structural health monitoring by means of strain field pattern recognition on the basis of PCA and automatic clustering techniques based on SOM

Peer Reviewe

Structural health monitoring and condition monitoring applications: sensing, distributed communication and processing

Structural health monitoring (SHM) is a wide area of engineering that focuses on the verification of the state or health of structures in order to ensure proper perfor- mance using nondestructive tests, involving sensors per- manently attached to the structure, how these sensors are distributed and computational algorithms. When a structure needs to be inspected, a SHM system is able to improve the quality of the analysis adding several advantages or benefits. Among these advantages are knowledge about the structural behavior under differ- ent loads and different environmental changes, as well as knowledge on the current state in order to verify the integrity of the structure and determine whether a struc- ture is able to perform properly or whether it needs maintenance or replacing (with the corresponding maintenance cost saving). In the same way, condition monitoring is a related area that allows the application of algorithms to evaluate and monitor parameters in machinery which are working together with the struc- tures. Both research areas consider the use of sensors installed in the system to monitor and develop data- driven and data fusion algorithms to evaluate the con- dition of the structures and the machinery.Postprint (published version

Transcriptional Activation of a Pro-Inflammatory Response (NF-κB, AP-1, IL-1β) by the <i>Vibrio cholerae</i> Cytotoxin (VCC) Monomer through the MAPK Signaling Pathway in the THP-1 Human Macrophage Cell Line

This study describes, to some extent, the VCC contribution as an early stimulation of the macrophage lineage. Regarding the onset of the innate immune response caused by infection, the β form of IL-1 is the most important interleukin involved in the onset of the inflammatory innate response. Activated macrophages treated in vitro with VCC induced the activation of the MAPK signaling pathway in a one-hour period, with the activation of transcriptional regulators for a surviving and pro-inflammatory response, suggesting an explanation inspired and supported by the inflammasome physiology. The mechanism of IL-1β production induced by VCC has been gracefully outlined in murine models, using bacterial knockdown mutants and purified molecules; nevertheless, the knowledge of this mechanism in the human immune system is still under study. This work shows the soluble form of 65 kDa of the Vibrio cholerae cytotoxin (also known as hemolysin), as it is secreted by the bacteria, inducing the production of IL-1β in the human macrophage cell line THP-1. The mechanism involves triggering the early activation of the signaling pathway MAPKs pERK and p38, with the subsequent activation of (p50) NF-κB and AP-1 (cJun and cFos), determined by real-time quantitation. The evidence shown here supports that the monomeric soluble form of the VCC in the macrophage acts as a modulator of the innate immune response, which is consistent with the assembly of the NLRP3 inflammasome actively releasing IL-1β

Non-linear damage classification based on machine learning and damage indices

The use of guided wave-based approaches presents some advantages in the structural inspection and damage identification processes. It is driven by the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects, however, its use in Structural Health Monitoring requires the development of efficient SHM methodologies to analyse and provide confident results. To do that, signal processing techniques for the correct interpretation of the complex ultrasonic waves are a need. In this sense, it is necessary to still work on the continuous search of methodologies for performing each one of the steps in the damage identification. As contribution, this paper presents a damage classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network. The document presents the description of the methodology including a description of the data reduction and the use of non-linear analysis of the information with hierarchical non-linear principal component analysis and some non-linear damage indices. The methodology is preliminary evaluated with a CFRP sandwich structure with some damages on the multi-layered composite sandwich structure which were intentionally produced to simulate different damage mechanisms, i.e. delamination and cracking of the skin. Finally, results are presented and discussed to remark the advantages and disadvantages of this methodology.Postprint (published version

Structural health monitoring by means of strain field pattern recognition on the basis of PCA and automatic clustering techniques based on SOM

Peer Reviewe