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• Ultrasonic guided waves (UGWs) in fluid-filled pipes are subject to more severe dispersion and coherent noise than those of vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called multistrategy hybrid dung beetle optimization based orthogonal matching pursuit (MHDBO-OMP) using dispersive Hanning-windowed chirplet model was proposed, of which the model not only represents the nonlinear frequency and asymmetric envelope of the dispersive UGW, but also contains the mode and propagation distance information, and this model exhibits better matching and interpretability than the other UGW models. For dispersive perturbed UGW signals with multiple modes and features, MHDBO-OMP achieves high reconstruction accuracy in both signal reconstruction (RES < 0.3) and propagation distance estimation (RES < 3 cm). When applied to experimental UGW signals obtained from water/oil-filled pipes containing cracks and corrosion pits, MHDBO-OMP successfully reconstructs dispersion-compensated signals that solely contain feature signals, and the minimum location error is 0.72 cm while the average location error is 2.65 cm. Ultrasonic guided waves (UGWs) in fluid-filled pipes are subject to more severe dispersion and coherent noise than those of vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called multistrategy hybrid dung beetle optimization based orthogonal matching pursuit (MHDBO-OMP) using dispersive Hanning-windowed chirplet model was proposed, of which the model not only represents the nonlinear frequency and asymmetric envelope of the dispersive UGW, but also contains the mode and propagation distance information, and this model exhibits better matching and interpretability than the other UGW models. For dispersive perturbed UGW signals with multiple modes and features, MHDBO-OMP achieves high reconstruction accuracy in both signal reconstruction (RES < 0.3) and propagation distance estimation (RES < 3 cm). When applied to experimental UGW signals obtained from water/oil-filled pipes containing cracks and corrosion pits, MHDBO-OMP successfully reconstructs dispersion-compensated signals that solely contain feature signals, and the minimum location error is 0.72 cm while the average location error is 2.65 cm. © 2001 Elsevier Science. All rights reserved. [ABSTRACT FROM AUTHOR]

• The reconstruction model is established for vibration signals. • The index vector is proposed to obtain all the feature coefficients. • The improve FISTA is proposed for a better performance in reconstruction. • The simulated signals and acquired signals are used to verify the effectiveness. We consider the compressive sensing reconstruction for vibration signals, which are complex due to the harsh working environment. The recent fast iterative shrinkage-thresholding algorithm (FISTA) has paved the way for the signal reconstruction with a low complexity and high efficiency. Unfortunately, when extending to the vibration signals, the current algorithm still has some drawbacks such as the bad reconstruction effect. In this paper, we propose the improved fast iterative shrinkage-thresholding algorithm (IFISTA) to improve the reconstruction effect. Under the new scheme, the reconstruction is promoted by extracting information from the unstable signals in the process of iteration. Then the feature coefficients will be protected from shrinkage during iteration. The effectiveness of the IFISTA is verified by simulated signals and acquired signals. It is showed that the proposed scheme has superior performance in reconstruction and feature protection for vibration signals. [ABSTRACT FROM AUTHOR]

• Compressed sensing based on DMWT combined with sample difference thresholding ensures that the technique is completely patient agnostic, does not require any prior information nor the QRS detector, and does not introduce DC offset between processed frames of the ECG signal. • The reconstruction quality can be improved by nonlinear quantization while the compression ratio isn't affected. • The BAT family, DB2, and DB4 multiwavelets are most suitable for Multiwavelet-based ECG compressed sensing with sample difference thresholding. This paper presents a novel Multiwavelet-based hybrid method for compressed sensing (CS) and reconstruction of ECG signals. The proposed method is patient-agnostic and does not require any prior patient-specific information. The proposed method removes low-frequency components by using multi-scaling wavelet functions, which sparsify the ECG signals in the multi-wavelet domain. A novel way of CS acquisition is used to acquire the high-frequency component of the ECG signal. The compressed sensed signal acquisition is performed based on sample difference thresholding. This approach ensures that a significant portion of the ECG signal will be acquired, without a need for a QRS complex or an R peak detector. The main compression is achieved by CS and linear and nonlinear quantization. Arithmetic encoding is used to improve compression further. The results show good reconstruction quality while maintaining a high compression ratio. The proposed method was evaluated for different multi-wavelet functions. [ABSTRACT FROM AUTHOR]

• An underwater coaxial scanning photon counting Lidar system based on two-dimensional turntable is developed. • A spatiotemporal correlation method based on instrument response function matched filtering preprocessing is proposed. • The ranging accuracy and resolution of the system are better than 1.0 cm. • An effective method are provided for high precision and high resolution 3D imaging of underwater targets. A coaxial scanning photon counting Lidar is developed to achieve three-dimensional (3D) imaging of underwater targets. A spatiotemporal correlation-based method is proposed to achieve high-precision detection, which involves preprocessing using matched filtering with the instrument response function. A single-point repeatability measurement of a planar target and a 3D image of a resolution plate are performed using the Lidar system and the signal reconstruction algorithm. The experimental results demonstrate that the ranging accuracy is better than 1.0 cm. Compared with the maximum likelihood estimation method, the proposed method exhibits higher ranging accuracy when the target distance does not change steeply. Additionally, the 3D imaging results of the target model consisting of a car and a suitcase can clearly distinguish features such as the rearview mirror, headlight, handle, and lock. The system has potential applications in underwater target identification, ocean engineering construction, underwater archaeology, and other fields requiring high-precision 3D imaging. [ABSTRACT FROM AUTHOR]

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that the learning-based models outperform traditional signal processing filtering in terms of pure inertial signal reconstruction. Moreover, they are shown to improve angular errors by one order of magnitude, given a navigation-related task. • A learning-based approach for denoising stationary accelerometers signals. • A proven improvement for downstream navigation tasks by one order of magnitude. • A comprehensive literature review of former related works to target our contribution. • An open-source access to both data and code, to easily reproduce the declared results. [ABSTRACT FROM AUTHOR]

• A new method called FTFM and its reconstruction are developed for transient feature extraction. • The proposed method adaptively synthesizes TFM basis and image sparse expression together. • FTFM achieves an effective but efficient manifold learning process. • FTFM-based reconstruction gains a natural compression and adaptive reconstruction effect. • Results show effectiveness of the proposed transient feature extraction method. The transient features caused by local fault are of vital importance for rotating machinery fault diagnosis, while they are always submerged and distorted by a large amount of noise interference and macro-structural vibrations. Time-frequency manifold (TFM) has been developed to extract these transient features in time-frequency domain, and the corresponding TFM-based data denoising has been further used to recover the time-domain transient signal. However, due to its time-frequency analysis and nonlinear manifold learning properties, there will be not only high computational cost for TFM learning but also a challenge for reliable transient feature recovery, which has further limited this technique to application in practical and on-line rotating machinery fault diagnosis. To overcome these problems, an improved fast TFM (FTFM) method is first developed to effectively but efficiently extract the transient characteristics. In the process of FTFM learning, a new time-frequency analysis technique called short-frequency Fourier transform (SFFT) is introduced to efficiently describe time-frequency distribution as a time-frequency image (TFI), and two-dimensional discrete wavelet transform (2-D DWT) is used to further compress the inherent time-frequency structure for nonlinear manifold learning. Subsequently, different from the conventional model-based sparse expression, by means of sparse latent components of FTFM basis, the corresponding signal reconstruction is later proposed in a series of inverse transformations, including the inverse SFFT developed and inverse 2-D DWT employed in this paper. The proposed FTFM-based reconstruction method indicates attractive prospects in the following two aspects: effective but efficient TFM learning for practical and on-line application, sound and adaptive signal reconstruction with the data-driven FTFM basis. Theoretical analysis and experimental verification using simulation and testing data indicated the computational efficiency and effectiveness of the proposed FTFM-based method for rotating machinery fault diagnosis. [ABSTRACT FROM AUTHOR]

• The paper proposes a generic framework which can incorporate any signal reconstruction based anomaly detection (AD) algorithm and unsupervised clustering / supervised classification algorithm. • The iVRIDA framework is proposed to detect rail vehicle running instability and to identify corresponding root cause. • Three AD algorithms namely: PCA-AD , SAE-AD and LSTMEncDec-AD algorithms incorporated in the iVRIDA for vehicle running instability detection. • The iVRIDA deployed on measured accelearation of a high-speed European rail vehicle. Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering algorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), Sparse Autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability occurrences. Furthermore, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events. [ABSTRACT FROM AUTHOR]

Vibration signal analysis is a significant approach in fault diagnosis. This article presents a new vibration signal preprocessing scheme called Automated Relative Energy based Empirical Mode Decomposition (AREEMD) and a novel Cepstral Autoregressive (cAR) feature extraction. AREEMD generates the modes based on high relative energies and automates the signal reconstruction. The cAR features take advantage of the cepstrum information and provide discriminant markers for various machine faults. The weighted K-nearest neighbor delivered the best results when validated on four different datasets. The proposed scheme yielded promising performances of average accuracies of 99.3% using SUBF, 97.4% for CWRU, 100% on the Ottawa, and 99.1% using the combined dataset. The significant generalizability of the technique is demonstrated by its high performance in identifying faults that will aid in the detection of early warnings, regardless of motor speeds and size of faults, assuring a reduction in system shutdowns brought on by bearing failure. [Display omitted] • A new in-house machine fault vibration dataset (SUBF v1.0) is created. • A novel AREEMD technique is devised for intelligent vibration signal preprocessing. • Novel powerful discriminative Cepstral Autoregressive Features are proposed. • Proposed framework is validated on SUBF, CWRU, Ottawa, and a combined dataset. [ABSTRACT FROM AUTHOR]

• A time–frequency feature-based electrical signal reconstruction method is proposed. • A target-simulated electric field minesweeper was designed based on a small unmanned boat platform. • The method achieves electrostatic signal and axial frequency signal reconstruction when the signal is incomplete. • Suitable for low signal-to-noise ratio marine acquisition environment or strong electromagnetic interference environment. • The developed method was tested on a scaled-down ship model and good performance was obtained. In recent years, with the continuous development of military unmanned boat technology, there has been a significant increase in interest in small unmanned boat-based minesweeper hunting technology. In this paper, an electrical signal reconstruction method based on time–frequency characteristics and used for small minesweeping gear is proposed. Compared with the existing methods, the method does not require measurement to obtain the complete electrical signal of the distribution and is applicable to both surface ships and submarines for signal reconstruction, which can complete the reconstruction of the original signal with high accuracy in the case of incomplete signal acquisition and can be applied to the signal reconstruction under the harsh marine acquisition environment or strong electromagnetic interference. In this paper, the mathematical model between the maximum sampling interval and the captain is established by extracting the time–frequency features of large ships, and the optimal size of the minesweeper and the optimal parameters of the point charge model are obtained based on the point charge model, and the performance of the method under low signal-to-noise ratio conditions is evaluated by depth permutation. Under the challenging signal-to-noise ratio conditions, the proposed method can accomplish the high-precision reconstruction of the electrical signal distribution of a large ship with the passage data collected by only one measurement node. From the results of simulation and ship model experiments, it can be seen that the electric signal can be simulated with a series unmanned boat array of one-tenth the size of a large ship, and the amplitude error of the reconstructed signal is less than 0.2 mV, the inversion error of EF(Electrostatic Field) is less than 10%, and the inversion error of SEF(shaft-frequency electric field) is less than 12%. [ABSTRACT FROM AUTHOR]

• A novel semi-supervised network is proposed for structural condition assessment integrated with deep autoencoder and pseudo-label propagation. • An innovative feature extraction strategy is propounded based on signal reconstruction via autoencoder. • An optimized fuzzy clustering algorithm is proposed for pseudo-label propagation. • Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted for validation. Insufficient labeled data of vibration measurement from unknown structural states cast great obstacle to attempt of extending the superiority of deep learning techniques into their rapid state evaluation. This study proposes novel semi-supervised networks for condition assessment integrated with deep autoencoder and pseudo-labels propagation. The architecture and mechanism of the workflow are elaborated. With sophisticated network design and novel strategies for improving performance, the proposed method succeeds to establish a systematic network embedded with simultaneously self-supervised autoencoder, optimized unsupervised fuzzy clustering and supervised classification algorithms in semi-supervised paradigm. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of identifying the post-earthquake damage scenarios. Driven by acceleration signals measured by merely single sensor, the prediction accuracy of proposed method achieved to be 0.95 in numerical validation and averagely 0.86 in laboratory case studies, respectively, confirming a powerful effectiveness and robustness. [ABSTRACT FROM AUTHOR]

[Display omitted] • We feed SSWT-decomposed components into the LSSVM without signal reconstruction to form an integral model. • We propose TS-LSSVM by introducing the structural sparse learning so as to adaptively select support vectors. • We collect a practical dataset and design TS-LSSVM algorithm to dynamically predict with a minimal training set. Accurate measurement of residual oxygen concentration in encapsulated pharmaceutical vials is adequate to ensure the quality of inner sterile preparations. However, the critical characteristic signal is feeble and covered by enormous environmental interference in the actual production. Inspired by structural sparse learning, we propose a novel prediction model in this paper, triple sparse least squares support vector machine (TS-LSSVM), in which the production priors are deeply excavated, and the feature, sample, and structure sparsity are realized simultaneously by redefining the objective function. In addition, the selection of support vectors can be adjusted adaptively according to the time-varying environmental noise, so as to ensure the reliability of long-term operation. First, the time–frequency components containing the prior knowledge are extracted based on the synchro squeezing wavelet transform (SSWT). Then, a triple sparse learning strategy is designed, which can accurately eliminate redundant wavelet coefficients and adaptively select training samples. Finally, a strict and fast optimization solution is proposed under the alternating direction method of multipliers (ADMM) framework. Experimental results on public and practical datasets prove the superiority of TS-LSSVM. [ABSTRACT FROM AUTHOR]

• ● A deep learning method is proposed to suppress the rebar clutters in GPR images. • ● The method is trained using unpaired GPR images in an unsupervised mode. • ● Contrastive feature encoder and similarity feature encoder are designed. • ● The method is validated by synthetic data, sandbox experiments, and field testing. The signals of reinforcing steel bars (rebars) in ground penetrating radar (GPR) images may mask defect signals underneath them, negatively affecting defect detection in reinforced concrete. This study proposes an unsupervised learning-based method trained using unpaired GPR images to suppress the rebar signal and reconstruct the defect signal on the recorded GPR B-Scan images. In particular, four contrastive feature encoders and two similarity feature encoders are designed in the network. The extracted features of contrastive feature encoders and similarity feature encoders are constrained by contrastive and similarity loss functions, respectively. The proposed method was validated using data from three scenarios: synthetic data, sandbox experimental data, and data collected from reinforced concrete in field. The results showed that the proposed method outperforms other unsupervised methods, and it can effectively suppress the rebar signals and accurately reconstruct the defect signals. Moreover, the identification accuracy of defect underneath rebars can be improved significantly. [ABSTRACT FROM AUTHOR]

With the rapid development of multi-source information fusion and multi-dimensional sensor technologies, the signal processing technology based on multi-channel signal has obvious advantages in noise elimination and signal reconstruction. In this paper, the correlation of four-channel signals is described by the quaternion domain, and a quaternion singular spectrum analysis method based on the convex optimization is proposed. Singular spectrum analysis (SSA) is used to analyze a single channel vibration signal in the phase space, which is performed by different singular spectrum characteristics of the trajectory matrix. For the signal processing in the quaternion domain, the trajectory matrixes developed by embedding procedure of four channels are employed to generate the augmented trajectory matrix. Then, the useful signal and unwanted signal such as noise can be distinguished by quaternion singular value decomposition (QSVD) to augmented trajectory matrix. It should also be noted that the convex optimization with non-convex penalty functions for QSVD is utilized to accurately estimate non-zero singular value, which can be set as regularization item in the matrix low-rank approximation. The proposed method is validated through numerical simulation signal and experimental signal. By comparing the results obtained from the proposed method, signal channel Fast Fourier Transform (FFT), SSA and multivariate empirical mode decomposition (MEMD), it is found that the application of the proposed model leads to a better solution to extract the feature frequency in mechanical fault diagnosis. [ABSTRACT FROM AUTHOR]

A linear interpolator with compressed output and its alternative version that supports error correction are presented in this paper. The interpolation devices can be driven by an Analog/Digital Converter (ADC) that accepts as input, low frequency signals, like sensor values. The proposed interpolation methods can correct ADC linearity errors and increase the dynamic resolution of an ADC or they can reconstruct a signal from fewer samples in non-uniform distance. If the input signal is partially exponential or logarithmic then, specific correction rules can be employed to achieve a lower signal quantization error. These rules are based on simple operations like shifts and comparisons and thus, they can be implemented using low complexity hardware. Spline, Linear, Quadratic interpolation, popular compression tools and reference signals are used to evaluate the experimental results. The proposed architecture is scalar since multiple interpolators can be connected in series. A 3-stage interpolator with 9-bit input and 12-bit typical output resolution was tested with sinusoidal input. The Signal to Noise and Distortion Ratio (SNDR) of the ADC that has been used was increased up to the double. The proposed method was also tested using a pressure sensor, an acoustic signal and image reconstruction leading to Mean Square Error (MSE) that is up to 10 times lower. The achieved compression ratio, ranges between 11% and 76%. The implementation complexity of a one stage interpolator with 9-bit input resolution requires 583 Logic Elements (LE) i.e., less than 3% of the LEs that exist in an Altera Cyclone III EP3C25N Field Programmable Gate Array (FPGA). [ABSTRACT FROM AUTHOR]

• The proposed CF method focuses on separating noise from fault data from sensors to reveal discriminative features. • This paper puts forward HSACNN, including SSAM, PSAM and HSAM. The attention mechanism of different angles is constructed, further improving the diagnostic effect of the model in the noisy environment. • CF-HSACNN is proposed and its optimal hyperparameter settings are obtained through BO. • A model interpretation method is explored. It is suitable for understanding the internal operating rules and parameter changes of the model under different conditions. State recognition of centrifugal fans is a challenging task. Because the difference of the fault signals collected is small in a noisy environment, it is difficult to capture useful features, which leads to a decrease in diagnostic efficiency. With the purpose of solving this problem, paper proposes a method that complete-spectrum fast entropy discrimination matches hybrid self-attention convolutional neural network (CF-HASCNN). The method uses fixed-point iteration to perform signal reconstruction on the vector of the complete spectrum decomposition, and uses fuzzy entropy threshold discrimination to weaken the interference of noise on the signal. Then, the HASCNN captures subtle useful information in two aspects: space and single point. In addition, the operating rules of the model are studied to provide an explanation basis. The method which has proposed has stronger anti-interference ability, compared with other advanced diagnostic models. [ABSTRACT FROM AUTHOR]

The paper deals with the distributed monitoring of harmonic and interharmonic pollution in electrical power delivery systems. In order to measure the disturbances and to identify the sources in a wide grid, a distributed measurement system with hundreds of measurement nodes has to be adopted. With the aim of obtaining the reduction of realization costs, the authors propose an innovative distributed architecture, based on cost-effective nodes, that takes advantage from the compressive sampling strategy. Differently from traditional approach, the network nodes will be only mandated to acquire and to digitize the line voltage directly in a compressed form, whereas a central measurement unit will receive data from all nodes deployed in the grid and will perform the successive signal reconstruction, making possible the use of low-performance hardware to realize them. The assessment of the compliance of the proposed measurement technique with the current power quality standards turns out to be mandatory, thus verifying the absence of artifacts introduced by the adopted compressed sampling approach. [ABSTRACT FROM AUTHOR]

• Quantifying the degree of mechanical property degradation for the steel after hydrogenation using HE index. • A nondestructive testing method based on pulse-echo ultrasonic measurement technique to estimate HE was proposed. • SWT-based signal reconstruction technique was used to improve the measurement accuracy of ultrasonic echo signal. • The feasibility of the method was verified by constructing ultrasonic characteristic parameters sensitive to HE. Nondestructive evaluation for the mechanical properties loss of equipment materials serviced in the hydrogen environment is particularly vital for monitoring the equipment operating conditions. In this study, the quantitative relationship between the introduced hydrogen content and the mechanical property degradation of 2.25Cr-1Mo-0.25V steel was investigated using electrochemical hydrogen charging technique and tensile testing, and a nondestructive testing method based on pulse-echo ultrasonic measurement technique, combined with the results of mechanical assessment for hydrogen embrittlement (HE) susceptibility, was proposed to indirectly estimate the degree of hydrogen-induced plasticity loss of the steel. Moreover, signal reconstruction technique was also used to improve the measurement accuracy of ultrasonic echo signal. The results of the study showed that hydrogen present in the metal lattice intensifies the energy attenuation of ultrasonic echo, verifying the feasibility and validity of the measurement method in the effective online prediction of HE susceptibility of the tested steel. [ABSTRACT FROM AUTHOR]

Accurate estimations of the time of arrival (TOA) are essential in ultrasonic nondestructive testing. The parameters of ultrasonic pulse echoes are usually estimated by detecting the peak value of the envelope with a Hilbert transformation (HT). However, ultrasonic pulse echoes are likely to be polluted by noise in the propagation process, distorting the envelope signal and preventing the TOA from being accurately estimated. This paper proposes an algorithm for estimating the parameters of ultrasonic echo signals based on joint sparse representation and the flower pollination algorithm (FPA). An improved matching pursuit algorithm is developed to eliminate the noise and improve the envelope extraction of the echo signals. This reduces the superposition of repeated signals and improves the reconstruction accuracy. The signal envelope is then extracted by an HT. Finally, FPA is used to fit the envelope and estimate the parameters. Numerical simulations and experimental results show that the parameters estimated by the proposed method are highly accurate and have a mean square error that is close to the Cramer–Rao lower bound. • An improved matching pursuit algorithm is developed to eliminate the noise and improve the envelope extraction of the echo signals. • Improved matching pursuit and the flower pollination algorithm (FPA) are employed to improve estimation accuracy. • We formulated certain basic theorems concerning construction of an overcomplete dictionary. • TOA is estimated with high accuracy, even in low SNR. [ABSTRACT FROM AUTHOR]

A novel fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Probabilistic Neural Network (PNN) is presented in this paper. It aims to achieve more accurate and reliable sensor fault diagnosis in thermal power plant. To restrain the mode mixing problem in traditional EMD, an MEEMD is proposed based on signal reconstruction and pseudo component identification. The MEEMD is applied to decompose the original thermal parameter signals into a finite number of Intrinsic Mode Functions (IMFs) and a residual to extract the sensor fault feature. After analyzing the inherent physical meanings of each IMF and residual, the variances of them are extracted as feature eigenvectors to express the signal feature. Finally, PNN is used as the classifier for detection and identification of sensor faults. Based on the practical normal signals, which are collected from a main steam temperature sensor of a CLN600–24.2/566/566 steam turbine, three types of representative sensor fault signals are simulated to test the proposed method. By analyzing simulation and real signal, the analysis results indicate that the MEEMD can restrain the mode mixing problem in traditional EMD effectively, and the proposed fault diagnosis method had better performance than the other two fault diagnosis methods including basic PNN and EMD–PNN. [ABSTRACT FROM AUTHOR]

Highlights: [•] An amplitude PDF model of EMF is built for slurry flow signal. [•] A signal reconstruction processing method is proposed. [•] High-low voltage switching mode based square-wave excitation. [•] A slurry-type electromagnetic flow transmitter is developed. [Copyright &y& Elsevier]

[Display omitted] • Wavelet-packet transform is used to reveal the differences between signals. • Multiscale-modular dictionary is trained parallel to increase the training speed. • Kurtosis is used to improve the accuracy of the sparsity adaptive matching pursuit. • The dictionary's adaptability is enhanced by the adaptive modular dictionary update. • The proposed method is verified through the tensile test based on intact rail. Compressed Sensing (CS) is an effective method for improving the real-time performance of crack-induced acoustic emission (AE) signals analysis in Structural Health Monitoring (SHM) of rails. Aiming to promote the reconstruction accuracy and speed of CS, a reconstruction method is proposed based on improved Sparsity Adaptive Matching Pursuit (SAMP) and modular dictionary update. In the proposed method, a multiscale-modular dictionary is devised based on a multiscale dataset to enhance the real-time performance of reconstruction. Meanwhile, the step size of the SAMP is adaptively adjusted by the kurtosis residuals, which promotes the reconstruction accuracy. Furthermore, to optimize the adaptability of the dictionary, kurtosis-deviation is utilized to update the dictionary adaptively and modularly. The proposed method was verified by tensile tests. The results demonstrate that the proposed method has a higher reconstruction accuracy and speed than other methods, which can guide real-time crack-induced signal analysis in the SHM of rails. [ABSTRACT FROM AUTHOR]

• Correlation matching is simple to achieve and has the anti-jamming ability. • Center frequency of jamming has the greatest influence. • Weighted kurtosis can be used as characteristic to reconstruct pulse components. • Improved singular spectrum analysis can effectively extract the pulse components. Considering the problem that the ultra-wideband detection system is susceptible to be jammed, the mechanism of narrowband suppressive jamming on ultra-wideband radio impulse detection radar is mathematically analyzed at first in this paper. As typical suppressive jamming, the noise amplitude modulation signal is selected as research object, and the average signal-to-jamming is defined as an evaluation index. Simulation results show that the center frequency of jamming signal has the most significant influence on the impulse signal among a series of optimal jamming parameters. Afterwards, combined with genetic algorithm, an optimized singular spectrum decomposition method is proposed to realize adaptive extraction of the impulse signal. Error of the reconstructed signal based on proposed method is much smaller than that of threshold methods. The results provide a reference for subsequent anti-jamming and waveform optimization. [ABSTRACT FROM AUTHOR]

• Transformation kernel is deduced to process multicomponent signal measured from rotating machinery. • Prior instantaneous frequency knowledge of the machinery component is not required. • Angle refinement step is developed to improve the capability of handling noise. • Chirp rates (angles) are adaptively selected by spectral kurtosis index. Time-frequency (TF) features of nonstationary vibrations are indicative of the health condition of rotating machinery and, are also pivotal in analyzing acoustic signals obtained from processes such as bat echo-location. However, the TF features in these nonstationary vibration and acoustic signals are often submerged by strong background noise. This article proposes using the refined matching linear chirplet transform (RMLCT) to enhance the TF features, where the chirp rates are adaptively determined by spectral kurtosis and only the interesting time-frequency representations (TFRs) are retained. With selected chirp rates, a novel transformation kernel is developed, enabling the proposed method to simultaneously process nonstationary multicomponent signals. Moreover, the angle refinement strategy is proposed to improve the noise-handling capability of the proposed method. The signal reconstruction of the RMLCT is also analyzed, demonstrating that signal components of interest can be accurately reconstructed. Numerical and experimental analyses validate the effectiveness of the proposed RMLCT. [ABSTRACT FROM AUTHOR]

• A novel thermography inspection post-processing scheme for electronic components. • A breakthrough in internal structure thermography inspection for highly-integrated and encapsulated components. • investigation of sliding window embedded scheme for detection contrast enhancement in thermography. • A structural identification and screening tool for the unverified electronic components cost-effectively. Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a generalization of multi-dimensional linear regression to facilitate multi-sensor fault detection and signal reconstruction through the use of analytical optimization. The proposed methodology is founded upon the solution of an optimal signal reconstruction problem. The technique is applied to the real time monitoring of exhaust gas temperature sensors and burner-tip temperature sensors, of a 14MW industrial gas turbine. Key benefits of the proposed technique are that it facilitates (i) real-time detection of sensor faults and the number of sensors that are at fault in a multi-sensor system; (ii) reconstruction of measurements that would normally be expected from the sensor at fault—thereby facilitating improved unit availability; (iii) determining the minimum number of non-faulty sensors that are required to be available to continue unit operation without unduly compromising performance. The use of an analytical formulation to determine (i–iii) means that the resulting technique incurs low computational overhead and is readily applied to real-time monitoring and subsequent remedial action. Experimental results demonstrate the efficacy of the developed procedures to facilitate continued unit operation in the event of sensor faults. Whilst the application to industrial gas turbine machinery is used to focus the study, it should be noted that the proposed techniques are much more widely applicable to numerous industrial and commercial systems. [Copyright &y& Elsevier]

Axial unbalance of the rotor is an important origin of disturbance torque for the GyroWheel (GW), which leads to constant torque and measurement drift. This paper proposes a novel axial unbalance identification strategy based on multi-position calibration. Based on the gimbal assembly support characteristics of the tilting rotor, the disturbance torque model caused by the offset from the support center to the centroid is established. Combined with multi-position calibration, the least square method is introduced to estimate the drift error coefficients, which are taken as the equivalent vibration response. The relationship between the unbalanced excitation by adding trial weight and the vibration response helps to calculate the original unbalance. To avoid the non-stationarity of the current signal affecting the estimation accuracy of the drift error coefficients, a signal process with consideration of CEEMDAN-IIT denoising is exploited. The experiments are performed to verify the superior performance of the proposed identification method. • Focus on the axial offset, an unbalanced torque model is established. • A multi-position calibration based unbalance identification strategy is proposed. • A new CEEMDAN-IIT based denoising method is exploited for signal reconstruction. [ABSTRACT FROM AUTHOR]

• Original algorithm for accurate reconstruction of BTT signals. • The maximum allowable signal bandwidth can be extended. • No strict sensor layout is required. • Blade vibration frequency is identified under strong noise. Blade tip timing (BTT) technique has become a promising way of online damage diagnosis for compressor and turbine blades by monitoring their vibrations. However, there are some disadvantages in previous algorithms for BTT signals, including not high precision, strict sensor layout, signal bandwidth limit, and weak anti-noise ability. To solve these problems, a novel interpolation method for wideband signal reconstruction (IWSR) is presented. Simulations are conducted using a lumped parameter model, which shows that IWSR reconstructs noiseless signals with overall errors mainly below 1%, and identifies the exact frequencies of 5 dB noise signals. In addition, the results validate its signal separation function and verify that its sensor layout is almost arbitrary, and its maximum allowable bandwidth is half the product of sensor number and rotational frequency. Experimental results demonstrate its effectiveness in frequency identification. In conclusion, IWSR provides precise information for real-time blade damage diagnosis, which benefits turbomachinery safety. [ABSTRACT FROM AUTHOR]

• The GRACE data were used to evaluate the changes in the Caspian Sea level. • GRACE 3D signal reconstruction has been performed to reduce errors and noise. • The minimum root mean squares error of the reconstructed GRACE data is ±3.8 cm. • The wavelet revealed a correlation between the GRACE and satellite altimetry. • The deep leering of GRACE has better compatibility with other sensors. The gravity recovery and climate experiment (GRACE) satellites detect changes in the distribution of water on the Earth surface based on the gravity anomaly. A correct reconstruction of the GRACE signal is still a challenging problem due to signal attenuation and noise levels. In this contribution, the GRACE data are analysed using a deep neural network to investigate changes caused in the Caspian Sea level (CSL). The novelty is to reconstruct the three-dimensional GRACE signal for reducing stripe errors, leakage error and gap filling. The reduction of the CSL was approximately 70 ± 0.2 cm from 2005 to 2016, with an annual trend of −6.77 ± 0.2 cm/year. The northern regions of CSL have a smaller annual amplitude than other regions. The proposed method has an average significant correlation of 82% with satellite altimetry and data collected by three tide gauge stations, thus showing good compatibility. [ABSTRACT FROM AUTHOR]

• Employ second-order synchroextracting to concentrate time-frequency representation. • Improve noise robustness through adaptive parameter method with maximum kurtosis. • The value are verified by simulation and vibration signal with rub-impact fault. The rub-impact fault between the rotor and the stator surface is a common and serious failure in rotating machinery. The vibration signals acquired during the rotating machinery operation will carry the time-varying characteristics of rapid fluctuating instantaneous frequency (IF) when the healthy condition of rotor system changes. The proposed method is termed as the general synchroextracting chirplet transform which is based on the classical chirplet transform and employs the second-order synchroextracting transform to concentrate the time-frequency representation (TFR). Moreover, an adaptive demodulation parameters determination method based on kurtosis is proposed. The proposed method is non-sensitivity to noise, allows for signal reconstruction, and retains certain processing ability for multicomponent signals. The effectiveness and practical value of the method are verified by the numerical signals and the vibration signals of the heavy oil catalytic cracking machine which has the rotor rub-impact fault. [ABSTRACT FROM AUTHOR]

• Compressive Sampling of electrocardiographic signals. • Signal-adapted sensing matrix. • Low computational complexity on the acquisition node. • Reduced data rate to be transmitted. • Accurate reconstruction of the signal waveform. The paper presents a novel method for the compressed acquisition of electrocardiographic (ECG) signals. The proposed method is intended to be applied to Internet-of-Medical-Things (IoMT) acquisition nodes (i.e. wearable measurement systems) as they can benefit from a reduction of the signal data rate to be transmitted, and the consequent reduction of energy consumption. Being based on Compressive Sampling (CS), the proposed method presents a very low computational complexity on the acquisition node. Moreover, since the sensing matrix is adapted to the acquired signal, it allows achieving a better reconstruction performance compared with the other CS-based methods available in literature. [ABSTRACT FROM AUTHOR]

• Relation model between RSNR and noise folding is derived in CS sampling system. • Relation model between RSNR and quantization bits is derived in CS sampling system. • The tradeoff between sampling rate and number of quantization bits is investigated. • The derived model has been evaluated with satisfactory results. A digital signal acquisition system consists of two steps: sampling and quantization. Sampling maps a continuous signal to a digital signal, which then is quantized into a finite number of bits. Generally, a high sampling rate can ensure robustness to noise, while high resolution means less distortion. However, an analog-to-digital converter (ADC) cannot provide a high sampling rate and high resolution simultaneously. The bit rate is constrained, and there is a tradeoff between sampling rate and resolution. In this paper, we investigate the signal reconstruction in the framework of a compressed sensing based sub-Nyquist sampling system. We also study the noise introduced in the sampling stage and the quantization stage and evaluate the recovered signal-to-noise ratio (RSNR) with respect to the sampling rate and resolution. Considering potential application, we study the tradeoff involved in choosing the sampling rate and number of quantization bits according to the input SNR. Finally, we derive a relationship between RSNR and signal sparsity order, sampling rate, and number of quantization bits. [ABSTRACT FROM AUTHOR]

• Thickness measurement method using terahertz wave in ambient atmosphere is required. • Echoes are required for thickness measurement lacking prior knowledge. • Echoes are buried under spurious oscillations caused by water vapor in time-domain. • A simple reconstruction algorithm is proposed, suitable for time-of-flight method. • Thickness of various samples are measured by the proposed algorithm. We propose a simple signal reconstruction algorithm for terahertz time-domain spectroscopy in ambient atmosphere for application to time-of-flight thickness measurement. Time-of-flight measurement method is a very basic method of measuring thickness, but it has the advantage of being very fast. Therefore, it is often used for rapid measurement in industrial environment. The echo signal is buried in the spurious oscillations of the ambient atmosphere because of water vapor. We successfully found the echo signal and measured the thickness of the sample, silicon wafers and various type of explosive materials, without any prior information in humid air. [ABSTRACT FROM AUTHOR]

• Wind turbine gearbox bearing vibration signals from a wind field are measured. • Proposed AVMD optimized by MAEE improves the decomposition accuracy. • Adaptive variational mode decomposition can reduce noise effectively. • AVMD-TEO can extract incipient fault feature of rolling bearings effectively. Extracting incipient fault features of rolling bearings is a hard work as the impact compositions in vibration signals are faint and disturbed by a lot of environmental noise. An adaptive variational mode decomposition and Teager energy operator method (AVMD-TEO) for diagnosing incipient fault of rolling bearings is proposed. Firstly, the minimum average envelope entropy is used as the fitness value to search the optimal parameters of VMD adaptively by grey wolf optimization algorithm. Subsequently, the efficient weighted kurtosis index is introduced to select the efficient modal components for signal reconstruction. Finally, the reconstructed signal is processed by Teager energy operator to enhance the faint transient impact compositions and identify the defect frequency. The superiority of AVMD in parameters selection compared with fixed-parameter VMD and maximum weighted kurtosis optimized VMD is verified by simulated signal analysis. Results from the cases show that the peak signal-to-noise ratio and fault characteristic coefficient obtained by the proposed method are increased by 8% to 229% and 37% to 258% respectively compared with some traditional methods. The proposed AVMD-TEO can effectively reduce signal noise and extract incipient fault feature of rolling bearings. [ABSTRACT FROM AUTHOR]

• Synchroextracted operator in SET is upgraded with 2nd order Taylor expansion. • A better approximation of local instantaneous frequency can be achieved in ASET. • Qualitative and quantitative evaluations of different methods are implemented. • ASET can be utilized to deal with vibration signal with time-varying nature. • Specific harmonics can be identified by ASET under variable operating conditions. This paper introduces an upgraded synchroextracting transform (SET), namely ameliorated synchroextracting transform (ASET), to deal with fast time-varying and strong frequency modulated signals for time and frequency analysis (TFA). SET is a recently developed time-frequency representation (TFR) method with an exceptional capability to enhance the readability of TFR, yet the kernel of SET is based upon the assumption of purely harmonic signal, therefore, it is more applicable to slow time-varying and weekly frequency modulated signals. However, for rotating machines in real practice, fluctuation loads and varying rotational speeds are always inevitable, the resultant vibrations will exhibit non-stationary frequency-modulated nature. To tackle this problem, in this paper, the synchroextracted operator in SET is upgraded in terms of second-order Taylor expansion to estimate the fast and strong frequency modulated signals so that a better TFR can be achieved. The advantages of ASET is demonstrated with simulated and experimental studies by comparing the existing time and frequency analysis methods. The sharpness of TFR, the ability of signal reconstruction and the robustness to noise pollution are also demonstrated. [ABSTRACT FROM AUTHOR]