EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks

TLDR: A novel EMI-based bolt looseness monitoring method incorporating both physical mechanism (acoustic attenuation) and data-driven analysis, by implementing a lead zirconate titanate (PZT) sensor network and a built-in graph convolutional network (GCN) model is presented.

Abstract: Electro-mechanical impedance (EMI) has been proved as an effective non-destructive evaluation indicator in monitoring the looseness of bolted joints. Yet due to the complex electro-mechanical coupling mechanism, EMI-based methods in most cases are considered as qualitative approaches and are only applicable for single-bolt monitoring. These issues limit practical applications of EMI-based methods in industrial and transportation sectors where real-time and reliable monitoring of multiple bolted joints in a localized area is desired. Previous research efforts have integrated various machine learning (ML) algorithms in EMI-based monitoring to enable quantitative diagnosis, but only one-to-one (single sensor single bolt) case was considered, and the EMI–ML integrations are basically unnatural and ingenious by learning the EMI measurements from isolated sensors. This paper presents a novel EMI-based bolt looseness monitoring method incorporating both physical mechanism (acoustic attenuation) and data-driven analysis, by implementing a lead zirconate titanate (PZT) sensor network and a built-in graph convolutional network (GCN) model. The GCN model is constructed in such a way that the structure of the PZT network is fully represented, with the sensor-bolt distance and sweeping frequency encoded in the propagation function. The proposed method takes into account not only the EMI signature but also the relationship between the sensing nodes and the bolted joints and can quantitatively infer the torque loss of multiple bolts through node-level outputs. A proof-of-concept experiment was conducted on a twin-bolt plate, and results show that the proposed method outperforms other baseline models either without a graph network structure or does not consider sensor-bolt distance. The developed hybrid model provides new thinking in interpreting sensor networks which are widely adopted in structural health monitoring, and the approach is expected to be applicable in practical scenarios such as rail insulated joints and aircraft wings where bolt joints are clustered.