Dispersive MUSIC algorithm for Lamb wave phased array

By controlling the excitation time delay on each element, the conventional phased array can physically focus signals transmitted by different elements on a desired point in turn. An alternative and time-saving strategy is that every element takes turns to transmit the excitation and the remaining elements receive the corresponding response signals, which is known as the full matrix capture (FMC) method for data acquisition, and then let the signals virtually focus on every desired point by post-processing technique. In this study, based on the FMC, a dispersive multiple signal classification (MUSIC) algorithm for Lamb wave phased array is developed to locate defects. The virtual time reversal is implemented to back propagate the wave packets corresponding to the desired focusing point and a window function is adopted to adaptively isolate the desired packets from the other components. Then those wave packets are forward propagated to the original focusing point at a constant velocity. For every potential focusing point and all receivers, the virtual array focuses the signals from all transmitters so as to obtain the focusing signals. The MUSIC algorithm with the obtained focusing signals is adopted to achieve Lamb wave imaging. Benefiting from the post-processing operations, the baseline subtraction as well as the estimation for the number of the scattering sources is no longer required in the proposed algorithm. Experiments on an aluminum plate with three artificial defects and a compact circular PZT array are implemented and the results demonstrate the efficacy of the proposed algorithm.

GC×GC-HRMS nontarget fingerprinting of organic micropollutants in urban freshwater sediments

Background Sediments are sinks for organic micropollutants, which are traditionally analysed by gas chromatography-mass spectrometry (GC-MS). Although GC-MS and GC-MS/MS (tandem MS) are preferred for target screening, they provide only limited chromatographic resolution for nontarget screening. In this study, a comprehensive two-dimensional GC-high-resolution MS method (GC×GC-HRMS) was developed for nontarget screening and source identification of organic micropollutants in sediments from an urban lake and channel in Copenhagen, Denmark. The GC×GC-HRMS data were processed by pixel-based chemometric analysis using baseline subtraction, alignment, normalisation, and scaling before principal component analysis (PCA) of the pre-processed GC×GC-HRMS base peak ion chromatograms (BPCs). The analysis was performed to identify organic micropollutants of high abundance and relevance in the urban sediments, to identify pollution sources. Tentative identifications were based on match factors and retention indices and tagged according to the level of identification confidence. Results The channel contained both significantly higher concentrations of micropollutants and a higher diversity of compounds compared to the lake. The PCA models were able to isolate distinct sources of chemicals such as a natural input (viz. a high relative abundance of mono-, di- and sesquiterpenes) and a weathered oil fingerprint (viz. alkanes, naphthenes and alkylated polycyclic aromatic hydrocarbons). A dilution effect of the weathered oil fingerprint was observed in lake samples that were close to the channel. Several benzothiazole-like structures were identified in lake samples close to a high-traffic road which could indicate a significant input from asphalt or tire wear particles. Conclusions Several chemical fingerprints of different sources were described in urban freshwater sediments in Copenhagen using a pixel-based chemometric approach of GC×GC-HRMS chromatograms. Various micropollutants of anthropogenic origin were identified. Tailored pre-processing and careful interpretation of the identification results is inevitable and still requires further research for an automated workflow.

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks

Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks. The results from preprocessing were generally of higher quality than what was achieved using a reference based on standardized methods (second-difference, asymmetric least squares, cross-validation). From 105 simulated observations, 91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-to-noise (SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene, paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept for the automated preprocessing of Raman spectra.

GC×GC-HRMS nontarget fingerprinting of organic micropollutants in urban freshwater sediments

Background: Sediments are sinks for organic micropollutants, which are traditionally analysed by gas chromatography-mass spectrometry (GC-MS). Although GC-MS and GC-MS/MS (tandem MS) are preferred for target screening, they provide only limited chromatographic resolution for nontarget screening. In this study, a comprehensive two-dimensional GC-high-resolution MS method (GC×GC-HRMS) was developed for nontarget screening and source identification of organic micropollutants in sediments from an urban channel and adjacent lake in Copenhagen, Denmark. The GC×GC-HRMS data were processed by pixel-based chemometric analysis using baseline subtraction, alignment, normalisation, and scaling before principal component analysis (PCA) of the pre-processed GC×GC-HRMS base peak ion chromatograms (BPCs). The analysis was performed to identify organic micropollutants of high abundance and relevance in the urban sediments and to identify pollution sources. Tentative identifications were based on match factors and retention indices and tagged according to the level of identification confidence. Results: The channel contained both significantly higher concentrations of micropollutants and a higher diversity of compounds compared to the lake. The PCA models were able to isolate distinct sources of chemicals such as a natural input (viz. a high relative abundance of mono-, di- and sesquiterpenes) and a weathered oil fingerprint (viz. alkanes, naphthenes and alkylated polycyclic aromatic hydrocarbons). A dilution effect of the weathered oil fingerprint was observed in lake samples that were close to the channel. Several benzothiazole-like structures were identified in lake samples close to a high-traffic road which could indicate a significant input from asphalt or tire wear particles. In total, 104 compounds and compound groups were identified. Conclusions: Several chemical fingerprints of different sources were described in urban freshwater sediments in Copenhagen using a pixel-based chemometric approach of GC×GC-HRMS BPCs. Various micropollutants of anthropogenic origin were identified. Tailored pre-processing and careful interpretation of the identification results is inevitable and still requires further research for an automated workflow.

Crack growth monitoring using fundamental shear horizontal guided waves

Monitoring cracks in critical sections of steel structures is a topic of growing interest. Existing high-frequency ultrasonic techniques have good detection sensitivities but poor inspection coverage, requiring an impractical number of transducers to monitor large areas. Low-frequency guided waves are used for corrosion detection in pipelines but are insufficiently sensitive for many crack detection applications. The sensitivity can be improved using higher frequencies and by placing the receiving transducers closer to the defect. This study evaluates the monitoring performance of an SH0 mode system at frequencies just below the high-order mode cut-off. Baseline subtraction with temperature compensation was applied to experimental data generated by a ring of transducers on a 6-in diameter pipe. It was found that the residual signals after baseline subtraction were normally distributed so the random fluctuations could be reduced by coherent averaging; it was thereby possible to reliably detect a 2 mm × 1 mm notch simulating a crack located one pipe diameter along the pipe from the transducer ring. The damage detection performance at different locations along the pipe was assessed by analysing receiver operating characteristic curves generated by adding simulated defects to multiple experimental measurements without damage. At a fixed standoff distance, the damage detection performance increases with the square root of the number of averaged signals and is also improved by averaging the signals received by transducers covering the main lobe of the reflection from the defect. When the defect is located more than about one pipe circumference from the transducer ring, the optimal performance is obtained by averaging across all the transducers in the ring, corresponding to monitoring the T(0,1) pipe mode. Therefore, an SH0 mode monitoring system has great potential for crack monitoring applications, particularly for welds in pipes.

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

Baseline subtraction is commonly used in guided wave structural health monitoring to identify the signal changes produced by defects. However, before subtracting the current signal from the baseline, it is essential to compensate for changes in environmental conditions such as temperature between the two readings. This is often done via the baseline stretch method that seeks to compensate for wave velocity changes with temperature. However, the phase of the signal generated by the transduction system is also commonly temperature sensitive and this effect is neglected in the usual compensation procedure. This article presents a new compensation procedure that deals with both velocity and phase changes. The results with this new method have been compared with those obtained using the standard baseline stretch technique on both a set of experimental signals and a series of synthetic signals with different coherent noise levels, feature reflections, and defect sizes, the range of noise levels and phase changes being chosen based on initial experiments and prior field experience. It has been shown that the new method both reduces the residual signal from a set baseline and enables better defect detection performance than the conventional baseline signal stretch method under all conditions examined, the improvement increasing with the size of the temperature and phase differences encountered. For example, in the experimental data, the new method roughly halved the residual between baseline and current signals when the two signals were acquired at temperatures 15°C apart.