Validation of a procedure for the evaluation of the performance of an installed structural health monitoring system

Abstract

Validation of the performance of guided wave structural health monitoring systems is vital if they are to be widely deployed; testing the damage detection ability of a system by introducing different types of damage at varying locations is very costly and cannot be performed on a system in operation. Estimating the damage detection ability of a system solely by numerical simulations is not possible as complex environmental effects cannot be accounted for. In this study, a methodology was tested and verified that uses finite element simulations to superimpose defect signals onto measurements collected from a defect-free structure. These signals are acquired from the structure of interest under varying environmental and operational conditions for an initial monitoring period. Measurements collected in a previous blind trial of an L-shaped pipe section, onto which a number of corrosion-like defects were introduced, were utilised during this investigation. The growth of three of these defects was replicated using finite element analysis and the simulated reflections were superimposed onto signals collected on the defect-free test pipe. The signal changes and limits of reliable detection predicted from the synthetic defect reflections superimposed on the measurements from the undamaged complex structure agreed well with the changes due to real damage measured on the same structure. This methodology is of great value for any structural health monitoring system as it allows for the minimum detectable defect size to be estimated for specific geometries and damage locations in a quick and efficient manner without the need for multiple test structures while accounting for environmental variations.

Compensation for temperature-dependent phase and velocity of guided wave signals in baseline subtraction for structural health monitoring

“…so the shape of the reflected signal is the same as that of the input signal) located at 0.72 m and with a peak amplitude of 5% of the pipe end reflection was synthetically added to the 50°C signal of Figure 2. This was done by superposition following a procedure proposed by Liu et al 25 and validated by Heinlein et al 26 The defect signal, the resulting ''damaged'' signal at 50°C, and the ''undamaged'' signal at 20°C are all plotted in Figure 5. Because the simulated defect is only 120 mm beyond the first weld, which is less than the length of the input wave packet, the weld and defect reflections partially overlap, producing the distorted reflection seen in the figure.…”

Location Specific Temperature Compensation of Guided Wave Signals in Structural Health Monitoring

“…In a PIMS setting, the data analysis typically involves comparing new measurements with the baseline records, where any change in signals could represent a defect signature. Alongside the conventional baseline subtraction method [13], recently some authors have proposed detection procedures based on the signal decomposition methods, such as singular value decomposition (SVD) [14] and independent component analysis (ICA) [15]- [17]. Unfortunately, most of these methods are hindered by the effects of changing environmental and operational conditions (EOCs), primarily temperature [18]- [20], which are also responsible for the changes in the signals, hence degrading the damage detection performance.…”

Change detection using the generalized likelihood ratio method to improve the sensitivity of guided wave structural health monitoring systems

“…such that the shape of the reflected signal is the same as that of the input signal). Such a signature can be scaled to obtain the desired peak amplitude of its envelope, that is set relative to the standard deviation of incoherent noise affecting the measurements, and can then be added to an undamaged signal at the desired location by superposition, following a procedure validated in Heinlein et al 37 The experimental setting described in section ''Experimental validation'' was used to guide the choice of the input signal and hence of the defect reflection, this being an eight-cycle, 23.5-kHz Hanning-windowed toneburst. Figure 2(a), (c) and (e) illustrates the generation of a current signal including a defect reflection whose peak amplitude equals the standard deviation of the incoherent noise.…”

“…The calibration curves were then used by LSTC to extract residual signals from the 100 “current” measurements shown in red in Figure 14(b). Since the pipe was left undamaged during the tests, introduction of damage was simulated by superposing onto the actual experimental signals the reflection expected from a defect producing uniform frequency response 36 at the desired amplitude, that is, following the procedure validated in Heinlein et al 37 and already used in section “Simulation of damage and application of GLR method.” A representative scenario among those analyzed in Figure 13 is considered, namely, the detection of a defect giving a reflection 1.5 times the standard deviation of incoherent noise affecting the measurements. As seen in Figure 16(a), the noise level was estimated at 0.04% with respect to the reflection from the end of the pipe; hence, a defect reflection amplitude of 0.06% was considered.…”