A Lightweight SHM Framework Based on Adaptive Multisensor Fusion Network and Multigeneration Knowledge Distillation

Abstract

Structural health monitoring (SHM) technology is of great importance to ensure the long-term and reliable operation of engineering structures. The monitoring systems for large structures are characterized by large numbers of measurement points and complex signal. In recent years, deep learning has been widely used in many fields, such as intelligent fault diagnosis and health monitoring due to its excellent feature extraction and nonlinear fitting ability. However, the existing deep networks for condition monitoring have some drawbacks. First, the multisensor information fusion is rarely considered, which is quite common in SHM systems. Second, most of the proposed models by current research are with large parameters and high complexity. To solve the problems mentioned above, we propose a lightweight SHM framework suitable for the multisensor monitoring tasks. In other words, the adaptive multisensor fusion network (ASF-Net) is proposed to dynamically mix with multichannel input samples and extract the efficient feature on different scales. Moreover, a joint loss function is established for network compression by transferring the knowledge from bulky teacher network to lightweight student network. Finally, to reduce the performance degradation during model compression, a multigeneration knowledge distillation (MGKD) strategy is proposed. The experiments on two SHM datasets show that the proposed method can achieve excellent classification accuracy, and model performance degradation of less than 2% can be obtained with at most 40.24 times less parameters and 128.82 times less computation, which has a great prospect on resource-constrained platforms.