Split Spectrum Processing Research Articles

Resolution Enhancement Method of L(0,2) Ultrasonic Guided Wave Signal Based on Variational Mode Decomposition, Wavelet Transform and Improved Split Spectrum Processing

Pipeline systems are prone to defects due to the harsh service conditions, which may induce catastrophic failure if found not in time. Ultrasonic guided wave (UGW) testing provides a convenient option for pipeline detection, showing high-efficiency, non-contact, long-distance and large-scale capabilities. To address the problem that UGW signals suffer from poor signal resolution that is mainly related to the coherent noise caused by the dispersion, multi-mode and mode conversion, an advanced signal processing method called VWISSP, based on variational mode decomposition (VMD), wavelet transform (WT), and improved split spectrum processing (ISSP) was proposed, of which SSP was improved by replacing the Gaussian filter bank with cosine filters of constant frequency-to-bandwidth and frequency-to-filter spacing ratios. Compared with ISSP, VWISSP shows better higher accuracy and resolution processing effects to noisy multi-defect UGW signals, which is manifested through the improvement of both the signal-to-noise ratio gain and the defect-to-noise gain. Only feature signals (defects and pipe end) are retained, whereas noise signals are eliminated completely.

Split-Spectrum Processing with Raised Cosine Filters of Constant Frequency-to-Bandwidth Ratio for L(0,2) Ultrasonic Guided Wave Testing in a Pipeline

Ultrasonic guided wave (UGW) nondestructive testing suffers from poor signal interpretation accuracy caused by the coherent noise that is related to dispersion, multi-mode and mode conversion. In order to split the L(0,2) UGW from the coherent noise, split-spectrum processing with raised cosine filters of constant frequency-to-bandwidth ratio (FBR-RC-SSP) is proposed. With the advantages of time domain resolution and frequency domain split, FBR-RC-SSP is studied based on time-frequency analysis using the chirplet transform, and the effects of filter parameters on signal-to-noise ratio gain (SNRG) and defect-to-coherent noise gain (DCRG) are explained. The excellent effects of eliminating the coherent noise and improving the signal resolution in FBR-RC-SSP are reported by the validation of synthesized, simulated and experimental UGW signals, of which the average SNRG and DCRG are 22.92% and 23.71% higher than those of traditional SSP using Gaussian filters, and it has the potential to locate and characterize defects in further UGW testing research.

On the Optimization of Spread Spectrum Chirps Into Arbitrary Position and Width Pulse Signals. Application to Ultrasonic Sensors and Systems

This work presents in detail a new method for the optimization of rectangular spread spectrum excitation signals for its use in simple and low-cost ultrasonic pulsers, but that could be extended to other applications based on spread spectrum signals. Starting from rectangular linear frequency modulated (RLFM) chirps, it uses the transfer function of the transmitted signal and received echoes from reference specimens to iterate through a recursive algorithm to obtain arbitrary position and width pulse (APWP) signals with the desired bandwidth, maximizing the energy and the flatness of the spectrum, and enhancing the resolution and dynamic range of conventional chirp excitation signals. This optimization procedure can be repeated for any transducer and material, so that it achieves the best performance in each experimental environment. Such characteristics are ideal for Time-of-Flight estimation, imaging, and applications in which spectral regularity is needed, such as the Split Spectrum Processing (SSP) algorithm, which is used as example to test the performance of the proposed excitation signals. It also allows to specify and change the bandwidth reducing the need to change the transducers. The method is tested with different transducers (2 and 5 MHz focused transducers) and complex composite materials (aluminum-carbon aviation composite and high porosity GFRP composite) in immersion setup for imaging applications using SSP algorithm.

Improved Defect Detection of Guided Wave Testing Using Split-Spectrum Processing.

Ultrasonic guided wave (UGW) testing is widely applied in numerous industry areas for the examination of pipelines where structural integrity is of concern. Guided wave testing is capable of inspecting long lengths of pipes from a single tool location using some arrays of transducers positioned around the pipe. Due to dispersive propagation and the multimodal behavior of UGW, the received signal is usually degraded and noisy, that reduce the inspection range and sensitivity to small defects. Therefore, signal interpretation and identifying small defects is a challenging task in such systems, particularly for buried/coated pipes, in that the attenuation rates are considerably higher compared with a bare pipe. In this work, a novel solution is proposed to address this issue by employing an advanced signal processing approach called “split-spectrum processing” (SSP) to minimize the level of background noise and enhance the signal quality. The SSP technique has already shown promising results in a limited trial for a bar pipe and, in this work, the proposed technique has been experimentally compared with the traditional approach for coated pipes. The results illustrate that the proposed technique significantly increases the signal-to-noise ratio and enhances the sensitivity to small defects that are hidden below the background noise.

Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method

Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split-spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30 dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection.

Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave

Ultrasonic guided wave (UGW) systems are broadly used in several branches of industry where the structural integrity is of concern. In those systems, signal interpretation can often be challenging due to the multi-modal and dispersive propagation of UGWs. This results in degradation of the signals in terms of signal-to-noise ratio (SNR) and spatial resolution. This paper employs the split-spectrum processing (SSP) technique in order to enhance the SNR and spatial resolution of UGW signals using the optimized filter bank parameters in real time scenario for pipe inspection. SSP technique has already been developed for other applications such as conventional ultrasonic testing for SNR enhancement. In this work, an investigation is provided to clarify the sensitivity of SSP performance to the filter bank parameter values for UGWs such as processing bandwidth, filter bandwidth, filter separation and a number of filters. As a result, the optimum values are estimated to significantly improve the SNR and spatial resolution of UGWs. The proposed method is synthetically and experimentally compared with conventional approaches employing different SSP recombination algorithms. The Polarity Thresholding (PT) and PT with Minimization (PTM) algorithms were found to be the best recombination algorithms. They substantially improved the SNR up to 36.9dB and 38.9dB respectively. The outcome of the work presented in this paper paves the way to enhance the reliability of UGW inspections.

Architecture of the reconfigurable ultrasonic system‐on‐chip hardware platform

Design of ultrasonic signal processing systems requires a paradigm shift to fully utilise the benefits of recent advancements in the field of integrated circuits. It is necessary to design a standardised common platform that provides the flexibility to develop both software and hardware solutions. This enables the user to explore the full design space including software only, hardware only, and hardware/software co-design. To fulfil this purpose, the authors introduce the reconfigurable ultrasonic system-on-chip hardware (RUSH) platform. RUSH provides a common basis which significantly reduces the effort required to develop an ultrasonic signal processing system able to process the full range of ultrasound from 20 kHz to 20 MHz. Furthermore, this study aims to make the design and implementation of signal processing algorithms in embedded software and reconfigurable hardware very efficient. To demonstrate the computational efficiency and design flexibility of the RUSH platform, several important computationally intense algorithms such as split spectrum processing, chirplet signal decomposition and coherent averaging have been successfully ported to the RUSH platform, emphasising the many parts of the RUSH architecture.

Adaptive split spectrum processing for ultrasonic signal in the pulse echo test

In this paper, an Adaptive Split Spectrum Processing technique (A-SSP) is proposed, to improve ultrasonic echoes detection. It is an arrangement of conventional Split Spectrum Processing (SSP) with an empirical method of analyzing nonlinear and non-stationary signals, called Empirical Mode Decomposition (EMD). This proposed technique allows breaking up the signal into several bands of frequencies in an adaptive way and intrinsic to the treated signal using EMD. It enables us to know the internal contents and the local changes of the ultrasonic signal and makes the detection of any desired targets more flexible for the coherent noise problem. In the combination phase of A-SSP, a linear operation for selected intrinsic mode functions and a non linear one for non selected intrinsic mode functions are used to reconstruct the signal with separated echoes. To evaluate the proposed techniques (A-SSP with different combination operations), firstly a mortar specimen with artificial defect is used to resolve the defects detection and localization problem. Secondly a paste cement specimen is also used to resolve the materials characterization problem. The signals were obtained using a technique applied in pulse-echo mode, known as the prism technique. Numerical and experimental tests were performed to verify the effectiveness and reliability of the proposed technique and to show its excellent performances.

Ultrasonic Flaw Detection in Composite Materials Using SSP-MPSD Algorithm

Due to the inherent inhomogeneous and anisotropy nature of the composite materials, the detection of internal defects in these materials with non-destructive techniques is an important requirement both for quality checks during the production phase and in service inspection during maintenance operations. The estimation of the time-of-arrival (TOA) and/or time-of-flight (TOF) of the ultrasonic echoes is essential in ultrasonic non-destructive testing (NDT). In this paper, we used split-spectrum processing (SSP) combined with matching pursuit signal decomposition (MPSD) to develop a dedicated ultrasonic detection system. SSP algorithm is used for Signal-to-Noise Ratio (SNR) enhancement, and the MPSD algorithm is used to decompose backscattered signals into a linear expansion of chirplet echoes and estimate the chirplet parameters. Therefore, the combination of SSP and MPSD (SSP-MPSD) presents a powerful technique for ultrasonic NDT. The SSP algorithm is achieved by using Gaussian band pass filters. Then, MPSD algorithm uses the Maximum Likelihood Estimation. The good performance of the proposed method is experimentally verified using ultrasonic traces acquired from three specimens of carbon fibre reinforced polymer multi-layered composite materials (CFRP).

GPR Raw-Data Order Statistic Filtering and Split-Spectrum Processing to Detect Moisture

Considerable research into the area of bridge health monitoring has been undertaken; however, information is still lacking on the effects of certain defects, such as moisture ingress, on the results of ground penetrating radar (GPR) surveying. In this paper, this issue will be addressed by examining the results of a GPR bridge survey, specifically the effect of moisture in the predicted position of the rebars. It was found that moisture ingress alters the radargram to indicate distortion or skewing of the steel reinforcements, when in fact destructive testing was able to confirm that no such distortion or skewing had occurred. Additionally, split-spectrum processing with order statistic filters was utilized to detect moisture ingress from the GPR raw data.

High speed, wide velocity dynamic range Doppler optical coherence tomography (Part IV): split spectrum processing in rotary catheter probes

We report a technique for blood flow detection using split spectrum Doppler optical coherence tomography (ssDOCT) that shows improved sensitivity over existing Doppler OCT methods. In ssDOCT, the Doppler signal is averaged over multiple sub-bands of the interferogram, increasing the SNR of the Doppler signal. We explore the parameterization of this technique in terms of number of sub-band windows, width and overlap of the windows, and their effect on the Doppler signal to noise in a flow phantom. Compared to conventional DOCT, ssDOCT processing has increased flow sensitivity. We demonstrate the effectiveness of ssDOCT in-vivo for intravascular flow detection within a porcine carotid artery and for microvascular vessel detection in human pulmonary imaging, using rotary catheter probes. To our knowledge, this is the first report of visualizing in-vivo Doppler flow patterns adjacent to stent struts in the carotid artery.

Signal quality enhancement using higher order wavelets for ultrasonic TOFD signals from austenitic stainless steel welds

Time of flight diffraction (TOFD) technique is a well-developed ultrasonic non-destructive testing (NDT) method and has been applied successfully for accurate sizing of defects in metallic materials. This technique was developed in early 1970s as a means for accurate sizing and positioning of cracks in nuclear components became very popular in the late 1990s and is today being widely used in various industries for weld inspection. One of the main advantages of TOFD is that, apart from fast technique, it provides higher probability of detection for linear defects. Since TOFD is based on diffraction of sound waves from the extremities of the defect compared to reflection from planar faces as in pulse echo and phased array, the resultant signal would be quite weak and signal to noise ratio (SNR) low. In many cases the defect signal is submerged in this noise making it difficult for detection, positioning and sizing. Several signal processing methods such as digital filtering, Split Spectrum Processing (SSP), Hilbert Transform and Correlation techniques have been developed in order to suppress unwanted noise and enhance the quality of the defect signal which can thus be used for characterization of defects and the material. Wavelet Transform based thresholding techniques have been applied largely for de-noising of ultrasonic signals. However in this paper, higher order wavelets are used for analyzing the de-noising performance for TOFD signals obtained from Austenitic Stainless Steel welds. It is observed that higher order wavelets give greater SNR improvement compared to the lower order wavelets.

Comparison of spread spectrum and pulse signal excitation for split spectrum techniques composite imaging

Ultrasonic imaging of composites was investigated. Glass and carbon fiber reinforced plastic produced by resin transfer molding and prepreg forming were analyzed. In some of the samples air bubbles were trapped during RTM (resin transfer molding) process and interlayer gaps were present in prepreg technology samples. One of the most expected techniques to apply in such case is the Split Spectrum processing. On the other hand such signals require specific processing to reliably reconstruct the temporal position of the defect reflection. Correlation processing can be used for signal compression or Wiener filtering can be applied for spectral content equalisation. Pulse signals are simple to generate, but lack the possibility to alter the signal's spectrum shape. Spread spectrum signals offer a powerful tool for signal energy over frequency band increase and resolution enhancement. CW (continuous wave) burst has high energy but lacks the bandwidth needed for SSP (spread spectrum processing). The aim of the investigation was to compare the performance of the above signals in case of composite imaging, when various Split Spectrum Processing techniques are used with preceding Wiener processing for spectral content compensation. Resulting composite signals and images obtained are presented. Structural noise removal performance was evaluated as Receiver Operating Characteristics (ROC).

A split spectrum processing of noise-contaminated wave signals for damage identification

A split spectrum processing (SSP) method is proposed to accurately determine the time-of-flight (ToF) of damage-scattered waves by comparing the instantaneous amplitude variation degree (IAVD) of a wave signal captured from a damage case with that from the benchmark. The fundamental symmetrical (S0) mode in aluminum plates without and with a notch is assessed. The efficiency of the proposed SSP method and Hilbert transform in determining the ToF of damage-scattered S0 mode is evaluated for damage identification when the wave signals are severely contaminated by noise. Broadband noise can overwhelm damage-scattered wave signals in the time domain, and the Hilbert transform is only competent fordetermining the ToF of damage-scattered S0 mode in a noise-free condition. However, the calibrated IAVD of the captured wave signal is minimally affected by noise, and the proposed SSP method is capable of determining the ToF of damage-scattered S0 mode accurately even though the captured wave signal is severely contaminated by broadband noise, leading to the successful identification of damage (within an error on the order of the damage size) using a triangulation algorithm.

System-on-chip design for ultrasonic target detection using split-spectrum processing and neural networks

Ultrasonic detection and characterization of targets concealed by scattering noise is remarkably challenging. In this study, a neural network (NN) coupled to split-spectrum processing (SSP) is examined for target echo visibility enhancement using experimental measurements with input signal-to-noise ratio around 0 dB. The SSP-NN target detection system is trainable and consequently is capable of improving the target-to-clutter ratio by an average of 40 dB. The proposed system is exceptionally robust and outperforms the conventional techniques such as minimum, median, average, geometric mean, and polarity threshold detectors. For realtime imaging applications, a field-programmable gate array (FPGA)-based hardware platform is designed for system-onchip (SoC) realization of the SSP-NN target detection system. This platform is a hardware/software co-design system using parallel and pipelined multiplications and additions for highspeed operation and high computational throughput.

New analysis and extensions of split-spectrum processing algorithms

In this paper we compare the performances of different variants of split-spectrum algorithms and propose some new extensions based on the use of variable bandwidth filters equally spaced in frequency and energy gain equalized. Signal-to-Noise Ratio Gain and Flaw-to-Clutter Ratio Gain factors were selected as the figures of merit to make the comparisons among the different methods. We considered simulated ultrasonic signals using both stationary and non-stationary models for the grain noise, and real scans obtained in laboratory from low dispersive (aluminum) and high dispersive (cement) materials. Frequency Multiplication (FM) recombination method is revealed as the best option when combined with the new extensions.

System-on-Chip Subband Decomposition Architectures for Ultrasonic Detection Applications

This paper presents a hardware efficient system-on-chip (SoC) sensor architecture for ultrasonic imaging applications that uses the split-spectrum processing (SSP) algorithm. The SSP design is realized using recursive subband decomposition techniques for achieving minimal hardware and power consumption. Recursive implementations of discrete Fourier transform (DFT) and discrete cosine transform (DCT) are presented for subband decomposition which result in sparse transform operations and significantly reduced hardware and power requirements. A comparative study and performance results present the advantages of the recursive hardware architecture compared to the conventional implementation of the SSP algorithm using IP cores for FFT.

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.

Comparative Investigation of Split Spectrum Processing over Classical Approach of Filtering for Non-Linear and Non-Stationary Signals

Ultrasonic welding defect signals are not processed, they are very difficult to identify the pattern of the defect, especially the TOFD welding defect signals are often mixed with noise and lead to confusion between grain noise and small flaws. It is very difficult to interpret the result defect and noise due to larger grain noise. This paper describes the comparative study of matlab simulation results of classic approach signal processing methodology and Split Spectrum Processing (SSP) with the Polarity Thresholding algorithm (PT) to the TOFD welding defect. A-scan signals are used to enhance SNR while suppressing the grain noise. The simulation results show that the welding defect TOFD signals after the SSP investigation are the best and produce the qualified output.

Automatic Crack Detection and Characterization During Ultrasonic Inspection

The creation of a non-destructive technique that enables the automatic detection of defects is desirable, and TOFD (Time-Of-Flight Diffraction) technique is gaining rapid prominence due to its high accuracy in detecting, positioning and sizing flaws in steel structures. In this type of imaging, cracks are characterized using sets of hyperbolas, where summit positions correspond to crack tip positions. However, ultrasonic diffracted signals are often too low and difficult to distinguish from noise, and when large structures are inspected, the quantity of data can be extremely large, with the area of interest being very small in comparison to the image size. This paper describes a method that avoids the image formation, replacing it with a sparse matrix (as there is no reason to store and operate on an excessive number of zeros), and automates crack detection by analyzing the curve formed by the sparse matrix elements. The sparse matrix is formed using Split-Spectrum Processing, which enhances the signal-to-noise ratio. The Randomized Hough transform is then applied on the sparse matrix elements to detect the hyperbolas that characterize the crack defects.