An improved adaptive harmonic decomposition method and its application to gear crack severity assessment

Gear crack is one of the most common and challenging fault modes in gearboxes. Accurate identification of its severity is critical for predictive maintenance and operational safety. However, the crack-induced impulsive component (CIIC) is typically very weak and difficult to extract from vibration signals. While adaptive harmonic decomposition (AHD) is a promising technique for extracting the CIIC from vibration signals, its diagnostic performance is fundamentally limited by a key bottleneck: the two core parameters, namely the harmonic intensity regulation parameter and the bandwidth constraint parameter, have to be determined empirically. This reliance on manual parameter tuning not only introduces subjectivity and inefficiency but also severely restricts the adaptability and reliability of AHD. To overcome this limitation, this article proposes an Improved AHD (IAHD) framework optimized by a novel hybrid metaheuristic algorithm. The proposed optimizer integrates the adaptive step-size mechanism into the Rüppell’s Fox Optimizer to achieve a balanced and efficient search for the optimal AHD parameters. The CIIC extracted by this adaptive framework is then fed into a convolutional neural network for crack severity assessment. Validation on both simulated and experimental gear crack signals shows that the IAHD achieves an assessment accuracy of 95%, significantly outperforming the conventional AHD method, which yields only an assessment accuracy lower than 70%. The results demonstrate that the proposed IAHD method successfully eliminates empirical parameter dependency, enabling automatic and superior feature representation for highly accurate gear crack severity assessment.