Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions

详细信息    查看全文

• 作者：Goutham R. Kirikera ; Vishal Shinde ; Mark J. Schulz ; Anindya Ghoshal ; Mannur Sundaresan and Randall Allemang
• 刊名：Mechanical Systems and Signal Processing
• 出版年：2007
• 出版时间：January 2007
• 年：2007
• 卷：21
• 期：1
• 页码：280-297
• 全文大小：1502 K

文摘

Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor–actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change.

Recently there have been advances in the development of a passive structural neural system (SNS) for damage detection. The SNS uses electronic logic circuits to mimic the signal processing in the biological neural system. The advantage of the SNS is that highly distributed continuous sensors provide high sensitivity to damage, and the biomimetic signal processing and passive sensing tremendously simplify the instrumentation and wiring of the monitoring system. Also, the SNS operates continuously during operation of the structure to detect ambient Lamb waves or bulk waves that are produced by cracking, delamination, bearing damage, rotor imbalance, flow instabilities, impacts, or other material failure modes.

In this paper, asymmetric Lamb wave propagation representing acoustic emissions (AE) is modelled based on a superposition of plate bending vibration modes. The simulation demonstrates that the SNS with four channels of data acquisition can localize damage within a grid of sensors irrespective of the number of sensors in the network. To experimentally validate the analysis results, a two-neuron prototype of the SNS was built and tested using a simulated AE source (a pencil lead break) on a riveted aluminium joint and on a composite plate. In both experiments, the SNS was able to localize simulated damages. These results indicate the feasibility of expanding the SNS to a large number of neurons.