In the discovering, identifying and mapping work of heritage objects in forest or desert areas, LiDAR ensures work efficiency and can provide the most complete and accurate 3D data. In the field of heritage documentation in China, the integration of LiDAR and small UAV is highly desirable. However, due to issues on the vibration of flying platform, load capacity, safety and other factors, not all UAVs can be used as LiDAR carriers. Therefore, the selection and design of suitable UAVs are very important. Little research has been done in this area and related experiments, complete test data and clear conclusions are hard to find. After long-term selection, design, trial-manufacturing and testing, the authors compare the vibration, capacity, reliability, stability of many UAV types, and finally develop two UAV platforms which are most suitable for carrying LiDAR for heritage mapping projects.

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research.