Online intelligent monitoring and diagnosis of aircraft horizontal stabilizer assemble processes

• Published in: International journal of advanced manufacturing technology Vol. 50; no. 1-4; pp. 377 - 389
• Main Authors: Du, Shichang, Xi, Lifeng, Yu, Jianbo, Sun, Jiwen
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.09.2010; Springer Nature B.V
• Subjects: Aircraft; Aircraft accidents & safety; Algorithms; Assembly; Business competition; CAE) and Design; Computer simulation; Computer-Aided Engineering (CAD; Control charts; Diagnosis; Engineering; Industrial and Production Engineering; Mechanical Engineering; Media Management; Neural networks; Original Article; Particle swarm optimization; Quality control; Signal monitoring; Signal processing; Simulated annealing; Stabilizers (fluid dynamics); Statistical process control; Statistical process control; Neural network; Horizontal stabilizer; Multivariate manufacturing
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-009-2490-0

The ability to reduce variation for quality improvement in the aircraft horizontal stabilizer assembly processes plays an essential role in the success of an aircraft manufacturing enterprise in today’s globally competitive marketplace. Monitoring and identifying variation source(s) of out-of-control signals are important issues for variation reduction in horizontal stabilizer assembly process. Traditional quality control focuses on statistical process control using control charts. However, control charts cannot identify variation source(s) of out-of-control signals. One novel integrated system is developed for monitoring and diagnosis of horizontal stabilizer assembly processes. control charts are firstly designed to be used as the detector of abnormal signals, and then, an improved particle swarm optimization with simulated annealing (PSOSA)-based selective neural network (NN) ensemble approach is explored for identifying the variation source(s) of out-of-control signals. Utilization of selective NN ensemble algorithm is able to improve the generalization performance of neural systems in comparison with using single NN recognizers, and PSOSA algorithm aims to improve the ability to escape from a local optimum. The data from the real-world aircraft horizontal stabilizer assembly processes are collected to validate the developed system. The results indicate that the developed system can perform effectively for monitoring and identifying out-of-control signals of variance increases in terms of correct classification percentage and average run length.