Work

A multi-criteria fusion-based pose estimation method for civil aircraft maintenance operation tasks

Year: 2024

Type: article

Abstract: To solve the problems of subjectivity, low feasibility, and use of a single assessment index in the traditional load assessment for civil aircraft maintenance (CAM) tasks. High-intensity workloads pose significant risks to the physical and mental health and safety of civil aviation staff. This work proposes the DNMEREC-DNCRADIS multi-criteria assessment method. DNMEREC-DNCRADIS is derived from the method based on the Removal Effects of Criteria (MEREC) and the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method. The digital model of the Boeing 737 aircraft engine is established. Then, Jack software is used to simulate the dynamic maintenance activities of aircraft maintenance workers, and eight types of simulation analysis data are used to assess their maintenance posture. Finally, the assessment data are analyzed using the DNMEREC-DNCRADIS method, and the overall priority scores for each maintenance task alternative are computed and ranked. The results demonstrate that removing the fan blade and fan blade shim at the 3 o'clock position imposes the lowest workload on maintenance personnel. In practical application, operation at the 3 o'clock position should be considered, or height-adjustable engine hangers should be adopted to adapt to the work requirements of different positions. This also can reduce the load caused by the maintenance personnel being in an awkward position for a long period, as well as other hidden dangers. The proposed method can be extended to the load evaluation of other types of operations. • A multi-criteria job risk assessment model is developed, considering multiple factors. • The results show that removing the fan blade and shim at the 3 o'clock position involves the least workload. • The rationality of the conclusion is proved by simulating the real scene experiment.

Source: Advanced Design Research

DOI: 10.1016/j.ijadr.2024.12.001

Authors: Huining Pei, Jingru Cao, Man Ding, Ziyu Wang, Yunfeng Chen, Fanghua Zhao

Institution: Hebei University of Technology

Language: English

Topic: Multi-Criteria Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid:

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hebei Province