Condition-based maintenance refers to the installation of permanent sensors on a structure/system. By means of early fault detection, severe damage can be avoided, allowing efficient timing of maintenance works and avoiding unnecessary inspections at the same time. These are the goals for structural health monitoring (SHM). The changes caused by incipient damage on raw data collected by sensors are quite small, and are usually contaminated by noise and varying environmental factors, so the algorithms used to extract information from sensor data need to focus on sensitive damage features. The developments of SHM techniques over the last 20 years have been more related to algorithm improvements than to sensor progress, which essentially have been maintained without major conceptual changes (with regards to accelerometers, piezoelectric wafers, and fiber optic sensors). The main different SHM systems (vibration methods, strain-based fiber optics methods, guided waves, acoustic emission, and nanoparticle-doped resins) are reviewed, and the main issues to be solved are identified. Reliability is the key question, and can only be demonstrated through a probability of detection (POD) analysis. Attention has only been paid to this issue over the last ten years, but now it is a growing trend. Simulation of the SHM system is needed in order to reduce the number of experiments.

This article explores the use of principal component analysis (PCA) and T² and Q-statistic measures to detect and distinguish damages in structures. For this study, two structures are used for experimental assessment: a steel sheet and a turbine blade of an aircraft. The analysis has been performed in two ways: (i) by exciting the structure with low-frequency vibrations using a shaker and using several piezoelectric (PZT) sensors attached on the surface, and (ii) by exciting at high-frequency vibrations using a single PZT as actuator and several PZTs as sensors. A known vibration signal is applied and the dynamical responses are analyzed. A PCA model is built using data from the undamaged structure as a reference base line. The defects in the turbine blade are simulated by attaching a mass on the surface at different positions. Instead, a progressive crack is produced to the steel sheet. Data from sets of experiments for undamaged and damaged scenarios are projected into the PCA model. The first two projections, and the Q-statistic and T²-statistic indices are analyzed. Q-statistic indicates how well each sample conforms to the PCA model. It is a measure of the difference or residual between a sample and its projection into the principal components retained in the model. T²-statistic index is a measure of the variation of each sample within the PCA model. Results of each scenario are presented and discussed demonstrating the feasibility and potential of using this formulation in structural health monitoring.

Obtaining the strain data all along the optical fiber, with adequate spatial resolution and strain accuracy, opens new possibilities for structural tests and for structural health monitoring. Formerly, only point sensors, as strain gages or fiber Bragg grating, were available, and information about the response to loads was restricted only to those points on which the sensors were bonded. Unless a sensor was located near the damage initiation point, details about the failure initiation and growth were lost. With a distributed system, the information is given as an array of data with the position in the optical fiber and the strain or temperature data at this point. In this article, the physical principles underlying the different techniques for distributed sensing are discussed, a classification is done based on the backscattered wavelength; this is important to understand its possibilities and performances. The definition of performance for distributed sensors is more difficult than for traditional point sensors because the performance depends on a combination of related measurement parameters. For example, accuracy depends on the spatial resolution, acquisition time, distance range, or cumulated loss prior to measurement location. The field of applications of this new technology is very wide; results of the structural tests of a 40 m long wind turbine blade, detecting the location and load of onset of buckling, and the results of the delamination detection in a composite plate, are presented as examples.

Fiber-optic sensors cannot measure damage; to get information about damage from strain measurements, additional strategies are needed, and several alternatives are available in the existing literature. This paper discusses two independent procedures. The first is based on detecting new strains appearing around a damage spot. The structure does not need to be under loads, the technique is very robust, and damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers wider coverage of the structure; it is based on identifying the changes caused by damage on the strain field in the whole structure for similar external loads. Damage location does not need to be known a priori, and detectability is dependent upon the sensor’s network density, the damage size, and the external loads. Examples of application to real structures are given.