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Multiscene delamination damages caused by the multiinterface characteristics of composites, including single delamination damage and overlapping compound delamination damage types, have a huge impact on the structural integrity and dynamic service performance of composite structure. Terahertz nondestructive testing (THz-NDT) technique, as a novel and high-resolution measurement method, has emerged great potentials in the integrity assessment of composite. However, current damage extraction methods in THz-NDT, including manual feature-based and data-driven approaches, are devoted to single delamination damage type, which brings a big challenge for overlapping compound delamination damages due to their complexity and unpredictability. In order to address the general characterization problem for multiscene delamination damages, in this work, a general multilabel THz intelligent characterization framework is proposed. The key is to simplify the general characterization problem of multiscene delamination damages into a multilabel classification task. This framework contains both specially designed core components: the multilabel attention fusion network (MLAF-Net) and the multilabel component encoding (MLCE) THz imaging strategy. Both components are used to perform the accurate localization and high-resolution imaging of multiscene delamination damages, respectively. Finally, a series of comparison and ablation experiments are carried out to demonstrate the advantages of the framework, which will provide a novel and general insight and paradigm for automatic localization and identification of complex multiscene damages in THz-NDT or other NDT fields.

The unbalance error of turbine rotor in aircraft auxiliary power unit (APU) can increase abnormal vibration and reduce service life. Therefore, the measurement and compensation of unbalance error of turbine rotor are crucial during the assembly process of aircraft APU. This article proposes an unbalance error compensation method of aircraft APU turbine rotor based on blade sorting. The method can reduce the residual unbalance by accurately adjusting the arrangement order of blades around turbine disk. To further compensate the unbalance error introduced by the assembly error of blades, the static unbalance error of turbine rotor is measured by the balancing machine after the first blade sorting and assembly, and the secondary optimization strategy is carried out by swapping several blades. The experiment results show that the proposed method can compensate the unbalance error introduced by turbine disk and blades. Especially, the secondary optimization strategy can compensate the unbalance error introduced by the assembly error and improve assembly precision. The static unbalance error of APU turbine rotor has been reduced from 15.0 to 2.68 g $\cdot $ mm after secondary optimization, and the assembly accuracy has been improved by 82.1%. Thus, the unbalance error compensation method proposed in this article can provide reference guide for intelligent assembly of blades in aircraft APU turbine rotor.