Early detection of fatigue crack damage in ductile materials: A projection-based probabilistic finite state automata approach

Fatigue failure occurs ubiquitously in mechanical structures when they are subjected to cyclic loading well below the material's yield stress. The tell-tale sign of a fatigue failure is the emergence of cracks at the internal or surface defects. In general, a machinery component has a finite fatigue life based on the number of cycles, it can sustain before a fracture occurs. However, the estimated life is generally conservative and often a large factor of safety is applied to make the component fail-safe. From the perspective of better utilization of a machinery component, it is, however, desirable to have maximum usage of the component without a catastrophic failure. It is, therefore, conducive to have a measure that can capture precursors to failure to facilitate active diagnosis of the machinery health. In this study, a precursor detection method is developed upon modifications of probabilistic finite state automata (PFSA). The efficacy of the proposed method is demonstrated on cold-rolled AL7075-T6 notched specimens in a computer-instrumented and computer-controlled fatigue testing apparatus. The results show that the proposed method is capable of detecting the emergence of cracks (at ~95% accuracy) and also can capture precursors with good fidelity.