Efficient temperature compensation strategies for guided wave structural health monitoring
Introduction
The detection of damage inside a structure is a generic problem for nondestructive testing (NDT) and has been extensively studied. Historically damage has been detected by temporarily placing sensors on the surface of a structure, performing an inspection of some kind, and then removing the sensors. This process is repeated if subsequent inspection is required. Structural health monitoring (SHM) represents a change to this basic approach. Sensors are permanently attached to the structure, allowing for highly accurate repeat measurements. This repeatability enables the recording of baseline measurements to track changes in the time-traces that can potentially be related to structural damage. The use of such baselines means that structures with complex geometries and responses can be monitored and a high degree of automation is possible. A further advantage of this approach is that no disassembly of the structure to be inspected is necessary, which can result in significant cost savings. Examples of SHM applications include damage detection in aircraft structures [1], bridges [2], offshore wind energy plants [3], pipes [4] and rails [5].
The current limiting factor of this SHM strategy is the difficulty in differentiating changes due to damage and those caused by changing environmental and operational conditions (EOC). Methods that have been developed in the last decades (for an overview see, e.g., [6], [7]) perform well in laboratory conditions but often fail in real-world situations where changing EOC are present. The underlying influences of temperature, humidity, changing boundary conditions, etc. can be sufficient to mask any changes due to damage to a degree that it might not be detected. It has been shown by Worden et al. [8] and Sohn [9] that damage-sensitive indicators are sensitive to EOC as well. Thus, it is important to focus on EOC in order to make damage assessment robust for in situ applications. In particular, expensive false alarms need to be avoided without decreasing damage sensitivity to enhance confidence in SHM technology.
Guided waves have shown great potential in SHM applications to detect damage in plate-like structures. They can travel over long distances and thus cover large areas with only a limited number of sensors. The fact that the entire thickness is interrogated makes it possible to detect damage inside the structure (e.g., cracks [10] and delaminations [11]) as well as on the surface (e.g., corrosion [12]). Of the EOC affecting guided waves, temperature has been shown to be one of the dominant effects [13]. In addition to altering the condition of the structure, temperature can also affect the transducers and their bonding. For small temperature variations of a few degrees, the effect of temperature on transducer performance has been shown to be significantly less than the effect of temperature on wave propagation within the structure [13]. Transducer bonding has also been shown to be remarkably consistent throughout fatigue tests lasting many weeks and tens of thousands of fatigue cycles [14]. For large temperature variations, changes in transducer and bonding properties may become more significant. However, this variability is one that can potentially be minimized via the choice of transducer types, materials and manufacturer. This is in contrast to the unavoidable effect of even small temperature changes on wave propagation within a structure, which is the subject of this paper.
Strategies for compensating the effect of temperature on the structure have been developed in recent years. In particular Lu and Michaels [15] and Konstantinidis et al. [16] introduced a methodology, often referred to as optimal baseline selection (OBS), that uses multiple baseline measurements recorded over a range of temperatures. This approach has been the basis for further developments (see [13], [16], [17], [18], [19], [20], [21], [22], [23], [24]), and a detailed discussion of this method will be presented in the next sections.
The purpose of this paper is to quantitatively analyze different methods of temperature compensation and show that their performance can be estimated from fairly simple models. The outline is as follows. Firstly, a simple mathematical description of the guided wave SHM process is presented and used to analyze the temperature compensation methodologies. Then results from two independent experiments are presented. From these the experimental performance of the different temperature compensation strategies is obtained and compared with earlier predictions. The analysis presented enables a quantitative assessment to be made of the efficacy of temperature compensation for specific guided wave SHM scenarios.
Section snippets
Temperature compensation
A typical guided wave SHM system comprises a number of transducers permanently attached to the surface of the structure. A suitable excitation signal is sent to one of the transducers and the time-domain responses (time-traces) from this and other transducers are recorded. This process is repeated using different transducers as the transmitter. Time-traces that are recorded on a different transducer to the transmitting one are referred to as pitch-catch mode, while those recorded on the
Experimental studies
Two completely independent experiments were carried out to investigate the practical performance of the techniques from the previous section. The first experiment was carried out at the University of Bristol (Bristol, UK). Sensors were optimized to preferentially excite and detect the A0 mode, and the experiment was carried out in an environmentally controlled chamber. The second experiment was performed at the Georgia Institute of Technology (Atlanta, GA, USA) and used transducers for which
Conclusions
This paper has quantitatively analyzed different temperature compensation strategies for guided wave structural health monitoring systems. It has been shown that a combination of optimal baseline selection (OBS) and baseline signal stretch (BSS) provides effective temperature compensation while using baseline datasets containing a relatively small number of time-traces. This approach has been demonstrated in two different experiments making use of both fundamental wave modes, and in both cases
Acknowledgements
This work was partly supported through the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant Number EP/C541960/1, and the US Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-08-1-0241. Jochen Moll’s placement in Bristol was funded through the “Stiftung der Deutschen Wirtschaft” scheme.
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