Validation of Damage Identification Using Non-Linear Data-Driven Modelling

Arredondo, Miguel Angel Torres; Burgos, Diego Alexander Tibaduiza; Buethe, Inka; Mujica, Luis Eduardo; Vejar, Maribel Anaya; Rodellar, José; Fritzen, Claus‐Peter

doi:10.4018/978-1-4666-5888-2.ch094

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…[46,47] During each actuation phase, a PZT transducer is defined as an actuator and a known signal is applied to the structure through this transducer; this electric signal is then converted into a vibration signal that travels over the structure and interacts with elements and damages in the structure. [48,49] After that, the propagated signals are collected from the rest of the sensors in the sensor network, these data is preprocessed with the DWT in order to obtain a lower dimensional representation of the information to reduce the computational cost of the training algorithm and make feasible its real implementation. This step can be seen as a feature extraction scheme.…”

Section: Training Stepmentioning

confidence: 99%

An acousto-ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

Torres-Arredondo

Burgos

2018

Struct Control Health Monit

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Summary In recent years, there has been an increasing interest in employing and analysing complex ultrasonic waves for the purpose of designing health monitoring systems for guaranteeing the safe structural operation by means of early damage prognosis strategies and reducing the costs of maintenance and downtimes. For this reason, monitoring systems are essential and require highly efficient techniques providing a probabilistic interpretation of their diagnostics. On that account, a Bayesian framework within the context of Gaussian processes is adopted in the present work for the purpose of probabilistic data‐driven modelling and damage detection. A network of permanently installed piezoelectric sensors in a pitch‐catch configuration is used for collecting the propagated ultrasonic waves together with the discrete wavelet transform for feature extraction purposes and matrix unfolding procedures such as the ones used in the monitoring of batch processes for sensor data fusion. The effectiveness of the proposed methodology is experimentally evaluated in three different composite structures. Receiver operating characteristics are used as a statistical measure to assess the damage detection capabilities of the presented method.

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Section: Training Stepmentioning

confidence: 99%

An acousto-ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

Torres-Arredondo

Burgos

2018

Struct Control Health Monit

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“…The self-organizing map (SOM) is a kind of unsupervised neural network also known as a Kohonen network [11] in honour of the professor Teuvo Kohonen. This neuronal network is specialized in visualization and analysis of high-dimensional data and has the special property of generating one organized map in the output based on the inputs, grouping input data with similar characteristics in clusters [12,34]. To do that, the SOM internally organizes the data based on features and their abstractions from input data.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

Data-driven methodology to detect and classify structural changes under temperature variations

Anaya¹,

Burgos²,

Torres-Arredondo³

et al. 2014

Smart Mater. Struct.

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This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet transform (DWT) from ultrasonic signals which are collected during several experiments. In addition, experiments are performed with the structure in different states (simulated damages), pre-processed and projected into the different baseline patterns for each actuator. Some of these projections and squared prediction errors (SPE) are used as input feature vectors to a self-organizing map (SOM), which is trained and validated in order to build a final pattern with the aim of providing an insight into the classified states. The methodology is tested 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 in both structures.

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Validation of Damage Identification Using Non-Linear Data-Driven Modelling

Cited by 2 publications

References 4 publications

An acousto-ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

An acousto-ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

Data-driven methodology to detect and classify structural changes under temperature variations

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