Fatigue crack detection and measurement play an essential role in the remaining useful life (RUL) estimation of sensitive structures. The current use of non-destructive testing (NDT) methods is usually limited to the out-of-service inspections and measurements. The ability to detect structural changes resulting from redistribution of the stress within components while in-service, makes acoustic emission (AE) a desirable NDT method for structural health monitoring (SHM). However, current approaches in AE signal analysis are usually based on some threshold-dependent features such as count and energy of the signals, which limit their utility. This research examines the potential of using a threshold-independent measure of AE signals based on the information content of the signals. The histogram of AE signals is studied to measure the Shannon information entropy. Cumulative information entropy of signals from two independent sensors placed in close vicinity of a crack is compared to two conventional threshold-dependent AE features. It is found that compared to cumulative energy and cumulative count, cumulative AE information entropy is less spatially dependent. The cumulative information entropy is shown to correlate well with crack length. This method provides a novel approach to measure the crack length in general, as shown specifically for aluminum alloy 7075-T6 material, using AE as a well-known NDT technique.
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