Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning

Abstract

The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt micro looseness monitoring method using deep learning was proposed. Specifically, the addition of batch normalization methods enables the established Batch Normalized Stacked Autoencoders (BNSAEs) model to converge quickly and effectively, making the model easy to build and effective. Additionally, using characterization functions preprocess the original response signal not only simplifies the data structure but also ensures the integrity of features, which is beneficial for network training and reduces time costs. Finally, the effectiveness of the proposed method was verified by taking the bolted connection structures of two key components of aircraft engines, namely bolt connection structures and flange connection structures, as examples.

Keywords

• bolt micro looseness monitoring
• characterization function
• stacked auto-encoders
• batch normalization