Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

Lamb wave-based damage detection is one of the most promising structural health monitoring (SHM) technologies for aircraft structures. In this paper, a Lamb wave-based deep transfer learning network is developed for multi-level damage classification of plate-type structures. A one-dimensional convolutional neural network (1D-CNN) is employed to deeply mine the damage characteristics of complex Lamb wave signals with multiple modes and multiple boundary reflections. The concept of multi-level damage classification is carried out to get different results for different engineers, and a multi-task cascaded 1D-CNN architecture is established for three levels of damage classifications, which is corresponding to different SHM levels, i.e. identifying the damage presence, location, and severity, respectively. In the multi-task cascaded architecture, a fine-tune transfer learning concept is adopted to share partial structures and weight values among different classification models, which could greatly improve the efficiency of the model calculation. In the multi-level damage classification model, the one-dimensional Lamb wave scattering signals with different damage locations and sizes are used as the input without any preprocessing steps, while the classifications of the damage presence, location and size are designated as output of different levels. An experiment has been conducted to verify the proposed multi-level damage classification model. The experimental results show that the training time of this model is reduced by 35% and the accuracy of the proposed model is greater than 99%, which verifies the effectiveness and reliability of the proposed technique.

Multi-parameter comprehensive sensing, as a data source for measuring and evaluating the physical state of devices, has become an important direction for the development of flexible electronics. In this case, temperature and pressure are the two common physical parameters, which are usually coupled; however, it is significant but challenging to decouple them simultaneously. Herein, a flexible double-parameter sensor is proposed to realize the decoupled measurement of temperature and pressure based on the thermal-resistance effect and piezocapacitive effect, respectively. A PEDOT:PSS/MWCNT serpentine electrode was prepared via the dispensing printing process to measure temperature, while a PVA/H₃PO₄ ionic film dielectric layer was prepared on porous conductive fabric via the electrostatic spinning process to sense pressure. The advantage of the proposed sensor is that the double dielectric layer capacitance for measuring pressure has a relatively large value and is sensitive to pressure, but not temperature, achieving the direct decoupling measurement of pressure and temperature in conventional measurements. The sensor layers were innovatively designed to enable the serpentine electrode for measuring temperature to be used as an electrode in piezocapacitive sensors. Finite element analysis was conducted to compare the sensitivity of the pressure measurement, realizing the optimized sensor configuration of top piezocapacitive layer and bottom thermal-resistance layer. The designed sensor was proven to have an extremely wide measurement range and high sensitivity. In the case of temperature, it could measure the temperature range of 15–80 °C, and the sensitivity below 50 °C was as high as 0.032 °C⁻¹. In the case of pressure, a wide measurement range of 0–600 kPa was demonstrated, with an extremely high sensitivity of 1249.34 kPa⁻¹ for low-pressure measurements below 10 kPa. The above-mentioned excellent performance proves that the proposed flexible sensor has significant potential application for the simultaneous measurement of temperature and pressure.

Fatigue crack growth in bolted metallic joints is one of the most serious and concerning types of damage in aircraft structures. The problem of how to quantitatively track the size of a crack in order to determine structural integrity has attracted much attention. In this paper, a novel eddy current array sensing film, with one exciting coil covering the entire thickness and several sensing coils distributed along the axial length of the hole, bonded onto the bolt, is proposed to quantitatively monitor a bolt-hole crack in the radial and the axial directions. Finite element simulation is utilized to investigate the eddy current disturbance caused by the crack growth and to optimize the coil configuration of both exciting and sensing coils in order to improve the crack monitoring capability of the sensing film. The simulation results show that the proposed sensing film has very good capability for tracking cracks when the traces of the exciting coil are designed to be similar to the sensing coils’ and have opposite current direction at the boundary of two adjacent sensing coils. An experiment is conducted to verify the feasibility and effectiveness of the proposed eddy current array sensing film to quantitatively track hole-edge crack growth in bolted joints.