Damage classification is an important issue within SHM going beyond the purely damage detection. This paper proposes a data-driven statistical approach for damage classification, which is constructed over a distributed piezoelectric active sensor network for excitation and measurement of vibrational structural responses. At different phases, a single piezoelectric transducer is used as actuator, and the others are used as sensors. An initial baseline model for each phase for the healthy structure is built by applying PCA to the data collected in several experiments. In addition, same experiments are performed with the structure in different states (damaged or not), and the dynamic responses are projected into the different baseline PCA models for each actuator. Some of these projections and damage indices are used as input features for a self-organizing map, which is properly trained and validated to build a pattern baseline model. This baseline is further used as a reference for blind diagnosis tests of structures. Both training/validation and diagnosis modes are experimentally assessed using an aluminum plate instrumented with four piezoelectric transducers. Damages are simulated by adding mass at different positions. Results show that all these damages are successfully classified both in the baseline pattern model and in further diagnosis tests. Copyright © 2012 John Wiley & Sons, Ltd.

One of the most important tasks in structural health monitoring corresponds to damage detection. In this task, the existence of damage should be determined. In the literature, several potentially useful techniques for damage detection can be found, and their applicability to a particular situation depends on the size of the critical damages that are admissible in the structure. Almost all of these techniques follow the same general procedure: the structure is excited using actuators, and the dynamical response is sensed at different locations throughout the structure. Any damage will change this vibrational response. The state of the structure is diagnosed by means of the processing of these data. Several studies have shown that the detection of changes in a structure depends on the distance from the damage to the actuator as well as the configuration of the sensor network. In this article, the authors considered the advantage of using an active piezoelectric system, where the lead zirconate titanate transducers are used as actuator and sensors in different actuation phases. In each actuation phase of the diagnosis procedure, one lead zirconate titanate transducer is used as actuator (a known electrical signal is applied), and the others are used as sensors (collecting the wave propagated through the structure at different points). An initial baseline model for undamaged structure is built applying principal component analysis to the data collected by several experiments and after the current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into the principal component analysis models. Two of these projections and four damage indices ( T²-statistic, Q-statistic, combined index, and I² index) by each actuation phase are used to determine the presence of damages and to distinguish between them. These indices are calculated based on the analysis of the residual data matrix to represent the variability of the data projected within the residual subspace and the new space of the principal components. To validate the approach, data from two aeronautical structures—an aircraft skin panel and an aircraft turbine blade—are used.

Objectives: The objective was the development of a method for the automatic recognition of different types of atypical lymphoid cells. Methods: In the method development, a training set (TS) of 1,500 lymphoid cell images from peripheral blood was used. To segment the images, we used clustering of color components and watershed transformation. In total, 113 features were extracted for lymphocyte recognition by linear discriminant analysis (LDA) with a 10-fold cross-validation over the TS. Then, a new validation set (VS) of 150 images was used, performing two steps: (1) tuning the LDA classifier using the TS and (2) classifying the VS in the different lymphoid cell types. Results: The segmentation algorithm was very effective in separating the cytoplasm, nucleus, and peripheral zone around the cell. From them, descriptive features were extracted and used to recognize the different lymphoid cells. The accuracy for the classification in the TS was 98.07%. The precision, sensitivity, and specificity values were above 99.7%, 97.5%, and 98.6%, respectively. The accuracy of the classification in the VS was 85.33%. Conclusions: The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.