Smart Autonomous Sensor Network for Multilevel Damage Identification

If there is anything industrial application can learn from nature, then it is flexibility and adaptiveness. Humanity has invented, designed and implemented a increasingly complex set of technological solutions to achieve more, make life more easy and reach further than our natural capacity allows us. However, only recently developments started to hand over the control to systems. While not disregarding the potentially negative sides of this, the concept of autonomous operation systems does exhibit a significant positive contribution to for example monitoring systems. Control of these systems includes the decision on what to monitor when and how and ultimately which action is to be taken based on the outcome of the monitoring. Normally, this is a task assigned to humans, which are generally considered as trustworthy and sufficiently flexible to recognize out of the ordinary responses. The objectiveness of the human judgment is however a weak point and the inability to recognize new, yet unknown outliers in a signal is increasingly pushed back by new technologies to resolve or circumvent this issue. Bringing this line of thought to the application of an actual application of a damage identification system for a safety critical composite structure, results in the concept of a smart autonomous sensor network using piezo-electric sensors. The use of piezo-electric sensors is crucial, due to their chameleon characteristic: they can be used as sensor, actuator and harvester. Focusing on the first two functionalities, a given set of piezo-electric transducers (PZTs) is embedded in a composite skin stiffener structure, which is excited by a shaker mimicking an operational, vibrating condition. In normal mode, the transducers measure the dynamic response of the system. Depending on several possible triggers, such as time, an impact event or anomaly identification based on the dynamic response, the mode of operation switches to a specific form of active, for example, Vibro-Acoustic Modulation (VAM) or acousto-ultrasonic (AU) measurement at a specific location in the network. All without human interference: all decisions on what to do when and how are taken by the control unit of the network. Ultimately, the outcome of the analysis of the system includes an operational control action, such as stopping the system (shut down) or limiting the maximum power. A leaner version simply issues a warning to the user that a specific component needs attention.