Sampling schedule optimization of embedded wireless sensors for degradation monitoring

Inexpensive wireless sensors can be embedded in structural materials to detect defects. These sensors provide in-situ diagnosis of the system's health, thus invaluable information to decision makers for system maintenance and repair. For example, lamb wave sensors that are embedded in carbon fiber composites can monitor the material integrity by detecting and quantifying fiber delaminations and breakages. Although they are relatively easy to be deployed, their lifetimes are limited due to power consumption and they cannot be replaced without interrupting the operation of system. In this paper, we discuss a sampling method that is based on the material's degradation model for activating sensors and collecting health information. We are interested in predicting the time of failure with a few numbers of signals and with statistical efficiency. Our method is good for the in-situ health monitoring, where the system's failure time is of concern and the sensor's power conservation is required.