Ultrasonic Inspection of Composite Materials Using Minimum Entropy Deconvolution

Composites exhibit higher strength and stiffness, better design practice and greater corrosion resistance compare to metal material. However, composites are susceptible to impact damage and the typical damage behaviour in the laminated composites is fibre-breakage and delamination. Detection of failure in laminated composites is complicated compared with ordinary non-destructive testing for metal materials as they are sensitive to echoes drown in noise due to the properties of the constituent materials and the multi-layered structure of the composites. In the current study, the detection of failure in multi-layered composite materials is investigated. To obtain a high probability of defect detection in composite materials, signal processing algorithms were used to resolve echoes associated with defects in glass fibre-reinforced plastics (GRP) detected by using ultrasonic testing. Pulse-echo method with single transducer was used to transmit and receive ultrasound. The obtained signals were processed to reduce noise and to extract suitable features. Results were validated on GRP with and without defects in order to demonstrate the feasibility of the method on defect detection in composites.

Deconvolution methods have been extensively applied for enhancing the impulsive feature of those bearing signals, which promotes the accuracy of the bearing fault diagnosis. However, these methods are sensitive to the invers filters length, which leads to poor effect. To overcome the existing problems, a new adaptive sparse representation-based minimum entropy deconvolution (AdaSRMED) is proposed on basis of the impulsive feature extraction of sparse representation, which promotes the robustness for inverse filter length and its effectiveness of impulse enhancement. In AdaSRMED, a new adaptive sparse representation (SR) is proposed based on the features of fault impulsive signals for solving these existing problems of previous SR methods. Moreover, the new adaptive SR is integrated with the minimum entropy deconvolution (MED) for improving the performance of MED, which avoids the shortcomings of MED and MED-related methods. A series of simulation signal and real fault signal experiments are performed to illustrate the superiority of AdaSRMED in fault detection. The comparison analysis with the conventional MED and improved MEDs shows that AdaSRMED can effectively enhance the impulsive features and show good bearing fault detection performance.

The problem of inspecting composite materials to detect internal defects is felt in many industrial contexts both for quality controls through production lines and for maintenance operations during in-service inspections. The analysis of the internal defects (not detectable by a visual inspection) is a difficult task unless invasive techniques are applied. For this reason in the last years there has been an increasing interest for the development of low cost non-destructive inspection techniques that can be applied during normal routine tests without damaging materials but also with automatic analysis tools. In this paper we have addressed the problem of developing an automatic signal processing system that analyzes the time/space variations in a sequence of thermographic images and allows the identification of internal defects in composite materials that otherwise could not be detected. First of all a preprocessing technique was applied to the time /space signals to extract significant information, then an unsupervised classifier was used to extract uniform classes that characterize a range of internal defects. The experimental results demonstrate the ability of the method to recognize different regions containing several types defects.

Box-cox-sparse-measures-based blind filtering: Understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution

The problem of detecting internal defects in composite materials is felt to be unavoidable in many industrial contexts both for quality control of production lines and for maintenance operations during in-service inspections. Among nondestructive techniques, thermographic image analysis has received much attention for the inspection of composite materials. In this chapter, we address the problem of developing neural network approaches for an automatic system of defect detection by the analysis of sequences of thermographic images. Neural networks are very promising because they offer the opportunity to associate input signals to output classes even in the case of nonlinear mapping. In particular, we have considered supervised and unsupervised approaches for training neural network and we have discussed the pros and cons of their applicability in systems that could help safety inspectors in the diagnosis of problems.

Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis

Various seismic deconvolution operators can be determined by estimating a seismic wavelet and subsequently designing an appropriate inverse filter which converts the wavelet to a spike. Seismic wavelets and deconvolution operators must be estimated in a time adaptive sense due to the nonstationarity of the seismic trace. The wavelet estimation methods considered in this paper either use the assumption of a minimum phase wavelet and a random impulse response, or the assumption that the wavelet cepstrum is readily separable from the cepstrum of the seismic trace. The former assumption is required in using the Hilbert transform and Wiener‐Levinson wavelet estimations, while the latter assumption is used in homomorphic deconvolution. These wavelet estimates can be used in the design of multichannel Wiener and Kalman deconvolution operators. Multichannel usage of homomorphic deconvolution can also be implemented through various types of cepstral stacking.The discussion of deconvolution filter design focuses on the problems of filter length, degree of prewhitening, and nonstationarity. In designing time adaptive deconvolution filters, the autocorrelation function can be used to monitor the nonstationarity of the seismic trace. The autocorrelation function, which is used in the computation of least squares inverse filters, can be estimated in an optimum fashion by using the maximum entropy method. Differences between minimum phase Wiener deconvolution and maximum entropy deconvolution become more pronounced for shorter data gates. As a result, the maximum entropy approach is preferred for time adaptive deconvolution.In this paper, the performance of various wavelet estimators and inverse filters is discussed using real and synthetic seismic data. Discussions of homomorphic deconvolution and maximum entropy prediction error filtering are merged with descriptions of conventional approaches to deconvolution.

We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid [Formula: see text]-norm loss function. This is in contrast to the SMBD, in which the hybrid [Formula: see text]-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques.

Pulsed thermography (PT) has emerged as a widely adopted technology for internal defect detection in carbon fibre reinforced polymer (CFRP) composites owing to its cost-effectiveness and rapid detection capabilities. However, defect identification in PT testing remains challenging because of the significant non-uniform heat distribution background and noise interference within the thermal images. This study introduces a novel thermographic defect detection approach that combines a multi-scale wavelet transform with sparse principal component thermography (MWT-SPCT). In this method, a multi-scale wavelet transform (MWT) is first applied to the thermal images for band-pass filtering. By leveraging the multi-resolution analysis capability of wavelet transform, the MWT simultaneously eliminates low-frequency background and high-frequency noise components. Following this stage, SPCT is employed to extract the features of defects based on the dynamic variation of defect signals throughout the sequence. Finally, through binarization of the extracted features, accurate defect segmentation and localization can be obtained. The experimental results demonstrated that, the MWT preprocessing selectively isolates defect information, thus increasing the signal-to-noise values of the defect signals in the filtered results. Compared to SPCT frameworks incorporating preprocessing methods such as contrast enhancement, background equalization, and signal smoothing, the proposed MWT SPCT framework yields significantly improved detection performance. Moreover, generalization evaluations demonstrate that MWT SPCT maintains consistently high accuracy and strong statistical stability across repeated measurements under different inspection conditions. This validates the reliability of the MWT-SPCT framework for defect detection under the influence of background and noise interference.

Ultrasonic NDE has been a well known approach to investigate material’s microstructures, mechanical properties and structure integrity in industry. The internal structure of a material and position of anomalies can be recognised by the reactions of different materials to ultrasound. However, the interpretation of ultrasound signals is difficult in composite material inspection task due to the fact that the ultrasonic pulse is reflected not only by the defect occurred within the material but the microstructures and multiple lay ups of the material. This phenomenon causes the backscattering noise to hinder the real defect’s signal during the inspection. Backscattering noise exists in multiple frequencies. The objective of this study was to develop a new noise reduction method to enhance the defect detectability in coarse-grained structure material such as composites materials. This method increases Signal-to-noise Ratio (SNR) by means of decomposing the original signal into multiresolution representations. To prevent the loss of information, the signal is processed in both temporal and frequency domain. The proposed method has been tested on simulated signal and Glass Fiber Reinforced Plastics (GFRP) laminates. Both simulation and experimental results showed that this method can significantly reduce grain noise while preserving the resolution of the original signal of the defect.

Digital twin approach for on-load tap changers using data-driven dynamic model updating and optimization-based operating condition estimation

Direct relationships between biological molecules connected in a gene co-expression network tend to reflect real biological activities such as gene regulation, protein-protein interactions (PPIs), and metabolisation. As correlation-based networks contain numerous indirect connections, those direct relationships are always 'hidden' in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co-expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a 'deconvolution filter' contain more genuine PPIs than before deconvolution.

Parameters of identification of surface crack signals Kh and Kw are proposed based on features of the Fourier spectrum and the wavelet spectrogram of modulation impulses defined using an eddy current sensor. Probability performances of defect detection using both spectral and wavelet methods are defined depending on the probability of a false alarm. Simulation data revealed that both methods have a good ability to identify short and shallow surface cracks. The wavelet method has the best probability of defect detection compared with the spectral method in case intensive noise. The spectral method is faster and preferable at small noise levels.

Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.

A deconvolution filter for multichannel nonminimum phase systems via the minimax approach