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This paper focuses on applying statistical process control techniques, based on principal component analysis, to vibration-based damage diagnosis. It is well known that localized structural damages with relative small amplitude do not affect much the global modal response of the structure, at least at low frequencies. Nevertheless, it can be expected that the local dynamic behavior of a damaged structural subcomponent is significantly affected. Assuming that each structural subcomponent is monitored, local structural damage, with relative small amplitude, will only affect a particular sensor without affecting significantly the response of the others. By applying a principal component analysis on the sensor time responses, it is possible to see that any change of one particular sensor will affect the subspace spanned by the complete sensor response set. The subspace corresponding to the damaged structure can then be compared with the subspace of an initial state in order to diagnose possible damage. The principal component analysis may also be performed for every potential subset of damaged sensors in order to identify the involved sensor, and, therefore, the damaged structural component. The spatial information given by the distributed sensors (e.g. piezoelectric laminates) can be used to forecast structural damages on a critical area but damage localization is also possible with classical sensors (e.g. accelerometers). The damage may be located as the errors attain the maximum at the sensors instrumented in the damaged substructures.

For a reliable on-line vibration monitoring of structures, it is necessary to have accurate sensor information. However, sensors may sometimes be faulty or may even become unavailable due to failure or maintenance activities. The problem of sensor validation is therefore a critical part of structural health monitoring. The objective of the present study is to present a procedure based on principal component analysis which is able to perform detection, isolation and reconstruction of a faulty sensor. Its efficiency is assessed using an experimental application.

This paper presents an application of statistical process control techniques for damage diagnosis using vibration measurements. A Kalman model is constructed by performing a stochastic subspace identification to fit the measured response histories of the undamaged (reference) structure. It will not be able to reproduce the newly measured responses when damage occurs. The residual error of the prediction by the identified model with respect to the actual measurement of signals is defined as a damage-sensitive feature. The outlier statistics provides a quantitative indicator of damage. The advantage of the method is that model extraction is performed by using only the reference data and that no further modal identification is needed. On-line health monitoring of structures is therefore easily realized. When the structure consists of the assembly of several sub-structures, for which the dynamic interaction is weak, the damage may be located as the errors attain the maximum at the sensors instrumented in the damaged sub-structures.