Health Monitoring and Diagnosis for Flexible Structures with PVDF Piezoelectric Film Sensor Array

Abstract

How to access the health situation of civil infrastructures is a brand-new challenge that scientists and civil engineers faced up to. Therefore, the method of health monitoring and diagnosis for flexible structures is a very active area of both academic and industrial research and development. For the large flexible structures, the mini cracks, which are the premise to induce the large cracks and damages in the structures, exert a little influence on the resonant frequencies of the structures. So the health monitoring method based on the changes of the resonant frequencies need that the cracks are so large that it can influence the resonant frequencies. However the mini cracks would change the vibration amplitude determined through monitoring the strain near the cracks, which can be used as the information sources realizing the health monitoring and diagnosis. It is a pity that the random external disturbances would influence the vibration amplitude, which can influence the effectiveness of the method that based on the vibration amplitude, so the key problem using the health monitoring and diagnosis method based on the vibration amplitude is how to avoid the influence of the random external disturbance. In this paper, a health monitoring and diagnosis method for flexible structures, which based on the relative outputs between sensors among PVDF piezoelectric film sensor array, is put forward. A Functional Link Neural Network (FLNN) is used as the damage modes classification unit. The experimental results show that the health monitoring method proposed in this paper is effective for diagnosing the damage and its severity, although the damage modes are not too complicated. Because the FLNN realizes the classification through enhancing the input patterns, the architecture of the neural network used in this paper is simple, the learning algorithm is easy, and the learning speed is fast. During the training and validation of the FLNN, its input patterns are formed by the relative outputs between the PVDF piezoelectric film sensors, which are affixed on the surface of flexible structures and formed into the sensor array. So the external exciting amplitude is not needed to be fixed or to be confined to a fixed variation range for the health diagnosis, which is validated by the experiment on the flexible beams and guarantees the method proposed in this paper to be suitable for a general usage.

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