Sensors are frequently used in damage diagnosis for structural health monitoring (SHM) of aerospace structures. This process typically requires a considerably large number of sensors. By increasing the number of sensors, the amount of data collected, though useful, becomes a burden due to the required computational overhead. In this paper, a random correlation cumulative approach is used to track damage intensity and propagation. The relative correlation between any two randomly chosen sensors is recorded and compared over time. The randomness of the process leads to detecting and tracking any arbitrary crack propagation. A case study for damage detection and tracking using 40 sensors in a steel plate is presented and discussed. It is shown that the proposed method can successfully allow damage tracking while limiting the data considered in the sensor network.

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