Structural health monitoring for mechanical structures using multi-sensor data

Structural health monitoring (SHM) is now one of the key and crucial issues for structures and machinery due to the increase in accident ratio amount. At present, more and more attention has been attracted toward the reliability and safety evaluations for the inspected structures. On reviewing the history of SHM, which is proposed in 1970s, we can see that the development of SHM always accompanies the development of sensor technologies and measurement technologies. Taking the measurement of strain and stress, for example, these two terms, which are important for damage detection in SHM, are usually registered by strain gauge or estimated by the derivative of displacement. However, the development of sensor allows the directed measure of the derivative modal shape, and the laser technology also allows the measure of the full wave field or vibration field. These are all fully involved and considered in the decision and the assessment of damage for SHM. The development of sensor technologies provides more alternative choices for SHM. Different from the classical approaches used in SHM, namely the acceleration-based methods, the novel methodologies contain the wave propagation method, fiber Bragg grating method, and the advanced modal curvature methods. These technologies detect damage via the totally different physical phenomenon. Thereafter, the determination of faults could be disparate. From the view of practice, more types and a larger number of sensors or array can be implemented on structures, thanks to the cost reduction of sensors and the higher requirement of reliability. This makes it possible to consider an SHM system based on multi-sensor data. On the other hand, the monitoring based on single-type sensor could not provide enough information to detect the operational condition of complex mechanical structures. The multi-sensor data SHM process includes data measurements taken using an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to predict the current state of the structures. ‘‘There are a thousand Hamlets in a thousand people’s eyes,’’ this also works for SHM, especially for the structures could be monitored by various types of sensors. The results of multi-sensor system may induce the conflict on conclusion due to the different sensitives of methodologies or phenomenon. To address the conflicts among methodologies, some novel methods for data fusion have been proposed, like the hybrid multivariate analysis method, the multiple step methods, and the hybrid methods. Although the present methodologies and techniques are feasible to resolve some problems, the emergence of nondestructive tests, SHM, and equipment evaluation using vibration information demands for the further development to achieve the essential data fusion. The appreciate processing of multi-sensor data should be based on the clear understanding of damage mechanism and modeling, and then the fusion of signal processing are all the crucial issue for this subject. On the aspect of damage mechanism and modeling, Xu et al. proposed fatigue mechanism–based dependent modeling with stochastic degradation and random shocks. They considered both fatigue degradation and applied random shock damage to have a coupled effect on the crack propagation process, which addresses the retardation phenomenon problem in multi-sensor SHM. Yang and colleagues proposed the wavelet finite element method to illustrate the relationships between crack propagation and the dynamic properties in frequency/wavenumber domain. The scaling functions and the corresponding wavelets functions are used to replace the polynomials utilized in traditional finite element modeling for higher accurate on the descriptions of crack and the other singularities. On the aspect of signal processing for multi-sensor data, Park et al. introduced a wireless displacement sensing system for