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This paper introduces a novel time–frequency analysis method called the Weight Extracting Transform (WET). The primary objective of WET is to enhance the energy concentration in linear time–frequency representations for time-varying signals. By aggregating the time–frequency blur produced by the short-time Fourier transform onto the actual instantaneous frequency, WET improves the readability and accuracy of the time–frequency representation. Additionally, the algorithm is extended to a more general linear time–frequency transform known as the chirplet transform, which effectively handles fast-varying signals. The WET method excels at distinguishing components with closely spaced instantaneous frequencies and can reconstruct the original signal from its time–frequency representation. Experimental results using simulated and real-world signals demonstrate that WET achieves superior energy concentration and noise robustness compared to existing methods. [ABSTRACT FROM AUTHOR]

• The closed form of bridge displacement induced by heavy vehicle is derived via frequency modulation technique. • Nonlinear frequency modulation signal reconstruction technique is adopted to separate the static and dynamic components. • Error analysis by theoretical derivation shows the effectiveness of simplifying the multi-axle heavy inspection vehicle. • Numerical simulations, laboratory tests and field measurements are presented to validate the proposed method. Bridge influence line plays an important role in evaluating bridge condition, which are usually identified by either time domain or frequency domain methods. Time domain approaches, while prevalent, encounter significant challenges in minimizing inspection durations and enhancing operational efficiency. Frequency domain methods often struggle with the accurate detection of signal peaks. Addressing these limitations, this study introduces an innovative time–frequency analysis technique, specifically, the application of nonlinear frequency modulation signal decomposition, to reconstruct bridge influence line from displacement induced by a heavy inspection vehicle. A comprehensive theoretical analysis is presented to derive the displacement response of a bridge subjected to a heavy inspection vehicle, as opposed to the conventional moving load paradigm. This displacement comprises two distinct components: a dynamic component, characterized as a typical frequency modulation signal, and a static component, which is directly proportional to the bridge influence line. Then the nonlinear frequency modulation signal reconstruction technique is proposed to separate the static and dynamic components and therefore extract bridge influence line. Moreover, error analysis is carried out through theoretical derivation to show the effectiveness of simplifying multi-axle inspection vehicle into a single-axle inspection vehicle. The study also incorporates numerical simulations to validate the derived bridge displacement, characterized by time-varying instantaneous frequency. These simulations confirm the feasibility of the proposed method in extracting the bridge influence line and enable a parametric analysis considering variables such as vehicle speed, type of inspection vehicle, and road surface roughness. To corroborate the theoretical and numerical findings, the study includes both laboratory experiments and field measurements. These validations highlight the efficiency and practicality of the proposed method. Notably, the method allows for inspection speeds of up to 60 km/h, significantly surpassing existing bridge influence line identification techniques, and obviates the need for constructing a vehicle information matrix. This study, therefore, presents a significant advancement in the field of bridge condition evaluation, offering a more efficient and practical approach to bridge influence line identification. [ABSTRACT FROM AUTHOR]

• A novel auxiliary active noise control system based on signal reconstruction is proposed. • A signal reconstruction model for multi-networks based on decomposition optimization is design. • The Bat Algorithm is used to optimize the weights of online AANC controller. • Results demonstrate that proposed system can balance modeling efficiency and accuracy. Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design; Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the VSS-LMS algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control. [ABSTRACT FROM AUTHOR]

• A new time-reassignment strategy inspired by ideal time–frequency analysis is proposed. • An efficient impulse feature extraction algorithm is derived from the sharpened time–frequency representation. • Algorithm performance is verified on complex bearing fault signals, such as variable speed conditions and multi-point defects. Rolling bearing fault signals and other types of impulse signals present transient broadband and fast time-varying characteristics in the time–frequency plane, which makes accurately locating the time–frequency trajectory of the signal a challenging task. In this paper, a new time–frequency analysis method is proposed to achieve a high energy-concentrated time–frequency representation for impulsive-like signals. At first, the points on the time–frequency ridges are detected to construct a fixed point set as the reassignment target of the fuzzy energy. Then the local blurred energy is directly assigned to the nearest ridge according to the reassignment direction of time–frequency points. The proposed method ensures a high concentration of energy and supports perfect signal reconstruction. The results of numerical validation prove that the new time–frequency analysis method has excellent noise robustness and energy concentration capability. Finally, the application of bearing fault diagnosis under multiple operating conditions confirms the excellence in the fault diagnosis field. [ABSTRACT FROM AUTHOR]

• An empirical method, SAM, is introduced for rolling element bearings diagnosis. • A nonlinear filtering process is proposed to separate components of a signal. • The filtering is achieved automatically and no frequency band selection is needed. Rolling element bearings are the critical parts of every rotating machinery and their failure is one of the main reason of the machine downtime and even breakdown. For this reason, various methods have been proposed in the past for their early diagnosis. Among them, envelope analysis and cyclic spectral analysis (CSA) are the most effective and widely used approaches, working according to the principle of linear filtering process of signals to remove undesirable components. However, in many cases machine structures are excited in different frequency ranges by impacts generated when defects are engaged, hence, the signal should be filtered for multiple frequency bands to completely extract the defect signals. Also, finding the proper frequency bands for demodulation is not always a simple task. To overcome these challenges, in this paper an empirical and automated nonlinear filtering process will be proposed in which different components of a signal are decomposed based on their powers. Then, their squared envelope spectra are computed to seek the presence of bearings characteristic frequencies. Therefore, this method can be seen as complementary to the narrowband amplitude demodulation techniques. The phase of each filtered component is similar to the phase of the original signal but the magnitude is transformed. The idea is that reconstruction of a signal only by its phase preserves many useful features. A similar approach is employed by cepstrum pre-whitening for bearings diagnosis to reduce the effect of powerful exogenous sources which mask the bearings signals and, despite its simplicity, has achieved noteworthy outcomes. Finally, the performance of the proposed method is validated on real case data recorded from two test rigs. Also, its effectiveness is investigated under constant and variable speed regimes and in presence of various level of Gaussian and non-Gaussian (highly impulsive) background noise. [ABSTRACT FROM AUTHOR]

• The HSWT method can effectively improve the TF energy concentration and obtain a high accuracy reconstruction for signals with fast varying IF. • The performance of the HSWT method has been theoretically proved. • The effectiveness of the HSWT method has been verified by a simulated signal and experimental signals. The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of signals with slowly varying instantaneous frequency (IF). However, the SST and its extensions provide poor time-frequency resolution for signals with wide frequency range and fast varying IF. In this paper, a new SST method called high-order synchrosqueezing wavelet transform is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) for nonstationary signals with wide frequency range and fast varying IF. This method uses high-order group delay and chirp rate operators to obtain the accurate estimation of instantaneous frequency. The proposed method can effectively improve the energy concentration of the TFR and remain invertible simultaneously. The numerical simulations investigate the performance and noise robustness of the proposed method when analyzing a typical amplitude-modulated and frequency-modulated (AM-FM) multicomponent signal. Finally, the application of planetary gearbox fault diagnosis in the variable operating condition verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Condition monitoring signals provide sufficient information about the health of machines and, therefore, are widely used for fault diagnosis, prognosis, and health management. Existing approaches generally extract one or more degradation features from original signals collected in a time interval and predict the remaining useful life of machinery based on a selected or fused feature under a pre-specified threshold. However, using a single feature is often inadequate in terms of the accuracy of fault prognosis due to the interval-based extraction procedure. To overcome the shortcoming, a new prognosis framework is proposed for machinery based on original condition monitoring signals in this paper. Technically, the Box-Cox transformation is first performed on the original signals point by point to construct a series of degradation features without losing information. Then, the neural basis expansion analysis for time series (N-BEATS) that has robust performance in time-series prediction is utilized to predict the future evolution for each feature with high volatility. By leveraging the similarity of multiple Box-Cox features, a parameter-based transfer learning method is proposed to reduce the computation complexity. Finally, we reconstruct the future original signals of machinery through the inverse Box-Cox transformation. Since the reconstructed original signals are noisy, a new failure criterion suitable for decision-level fault prognosis is defined from an industrial application perspective. An application on high speed train wheels and two follow-up simulations are used to illustrate the performance of our proposed framework in machinery fault prognosis. [ABSTRACT FROM AUTHOR]

Poorly assembled or faulty rotors often behave nonlinearly. The accurate extraction of weak nonlinear features is a vital issue. This paper proposes to extract the partial dynamic properties of concern for nonlinear feature based rotor condition monitoring. The realization of this concept relies on a novel tailored data-driven NARX (Nonlinear Auto Regressive with eXogenous input) modelling approach. The tailored NARX model represents part of the system's dynamic properties of concern. The details are first to use the harmonic product spectrum to determine the rotational speed of the inspected rotor system. After that, the least mean squares method is used to extract the harmonic components from the measured vibration signal. The low-order harmonics of concern are then reconstructed for the tailored NARX modelling. Then the Nonlinear Output Frequency Response Functions are evaluated from the tailored NARX model as features for rotor condition monitoring. Finally, the proposed method is validated by simulation and experiment cases. The results show that the obtained features based on the proposed tailored NARX modelling approach are more robust and more accurate than conventional methods when assessing rotor faults. The proposed approach provides a reference for the inspection of the assembly quality of components such as bearings and the on-line health monitoring of rotating machinery. [ABSTRACT FROM AUTHOR]

Highlights • A modified method called CSA-VMD is proposed for the purpose of noise reduction. • OSMSB is presented for removing stochastic noise and highlight fault characteristics. • A new fault diagnosis scheme consisting of CSA-VMD and OSMSB is formulated. • Simulated and experimental examples proved the effectiveness of the proposed method. Abstract The bearing vibration signal with strong non-stationary properties is normally composed of multiple components (e.g. periodic impulses, background noise and other external signal), where periodic impulses relevant to bearing fault are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent fault features. Variational mode decomposition (VMD) is a recently introduced approach for analyzing multi-component signal and is formulated based on the classic Wiener filter. However, some challenges remain when VMD is employed to process the real non-stationary signal. The most significant aspect of difficulties is that the inside parameters of VMD demand to be set manually in advance. To overcome this weakness, a modified method known as cuckoo search algorithm-based variational mode decomposition (CSA-VMD) is proposed in this paper, which can decompose adaptively a multi-component signal into a superposition of sub-signals termed as intrinsic mode function (IMF) by means of parameter optimization. In addition, a spectrum analysis technique called optimal scale morphological slice bispectrum (OSMSB) is presented for extracting fault symptoms from the susceptive mode components, which can restrain the Gaussian noise to a great extent and enhance the fault characteristics. Finally, a new fault diagnosis scheme consisting of CSA-VMD and OSMSB are compared with the existing methods (e.g. CEEMD-FWEO and fast kurtogram) by the application of simulation signal and experimental data, and the comparative results verify the effectiveness and superiority of the proposed method in extracting bearing outer race fault symptoms. The studies provide a novel perspective for the improvement of bearing outer race fault detection. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the laser displacement sensor (LDS) has been widely applied to a multitude of fields such as precision measurement and reverse engineering. However, the accuracy of the LDS applied to these industries is severely restricted by signal processing methods including signal compensation and signal reconstruction. In view of this, a B-spline curve approximation is used to realize high-precise and efficient reconstruction of noisy signals of the LDS and an improved elitist clonal selection algorithm (ECSA) is proposed to address the multi-objective, continuous and nonlinear optimization problem in the B-spline curve approximation process, namely the knot adjustment problem. Additionally, an adaptive chaotic mutation operator is designed to improve the algorithm search efficiency, increase the population diversity and avoid the prematurity. And an antibody reselection strategy based on the antibody concentration and the antigen affinity vectorial moment is proposed. Subsequently, the Hannan-Quinn information criterion (HQIC) is used as an affinity measurement to judge and weigh the goodness of fit and computational complexity and to automatically and accurately calculate the number and location of internal knots, thereby reconstructing noisy signals with different features. Simulation results evidence that the improved algorithm can not only realize the automatic B-spline curve reconstruction of the noisy signals featuring continuity, discontinuity and sharp points but also surpass existing studies in the global convergence and convergence rate. And both the on-machine measurement experiment for API threads and the comparison experiments for measured thread parameters validate the excellent and powerful performance of the proposed method in processing LDS signals. [ABSTRACT FROM AUTHOR]

Highlights • A time-wavenumber-domain method is proposed to eliminate the Doppler effect. • Signals after eliminating the Doppler effect are used to reconstruct source signals. • The proposed method can work stably at high speeds if increasing the array aperture. Abstract The time-domain signals of noise sources are of great importance to acoustic design of machinery. One available way to obtain source signals is to reconstruct them from the acoustic signals measured by a microphone array. However, when sources are moving, the acoustic signals sampled by a fixed microphone array are distorted in amplitude and frequency due to the Doppler effect, leading to that the reconstructed source signals may not reflect the real information of sources. This paper first presents a time-wavenumber-domain method to eliminate the Doppler effect in the measured acoustic signals, in which no priori information on the number, frequencies and locations of sources is needed. Then the acoustic signals after eliminating the Doppler effect are used as the input of the passive time reversal method to reconstruct source signals. Numerical simulation and experimental results show that the proposed method can effectively eliminate the Doppler effect and accurately reconstruct the source signals even when the sources emit transient signals. It is also found that the proposed method can work stably when the sources are moving at high speeds if increasing the array aperture and in the situation of low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Highlights • We propose a high-resolution time-frequency analysis method that can precisely characterize the time-varying signal. • The proposed method allows for perfect signal reconstruction and mono-component mode decomposition. • The numerical and experimental examples are employed to validate the proposed method. Abstract Time-frequency (TF) analysis (TFA) is an effective tool to analyze time-varying signals. The reassignment method (RM) and synchrosqueezing transform (SST) are high-resolution TFA methods that have attracted much attention recently in the signal-processing field. However, the RM and SST methods suffer from some drawbacks, such as unavailable signal reconstruction and poor energy concentration that hinder their applications in real-world data analysis. To overcome these drawbacks, we propose a novel SST-based technique that can achieve more concentrated representations than RM and SST. Meanwhile, we prove that it allows for perfect signal reconstruction. Furthermore, the ridge detection method and synchroextracting operator are combined to form an adaptive mode decomposition algorithm. The numerical validation shows that the proposed method can be used to sensitively discover the amplitude-weak modes and effectively address signals with heavy noise. In the experimental validation, we first analyze the gravitational-wave (GW) signal. The analysis results show that the proposed method can provide a better time-varying description for the generation procedure of the GW signal. Moreover, the reconstructed signal has a high consistency with the general relativity proposed by Einstein. The experimental analysis on fault bearing shows that the proposed method can discover more detailed features that are helpful for diagnosing bearing faults. Comparisons with other TFA methods demonstrate the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • Paper provides a framework for probabilistic compressive sensing. • The paper aims to address two short-comings of the traditional formulations: the ad-hoc manner for tuning sparsity levels in signal reconstructions, and the lack of uncertainty quantification. • The key idea being presented is that the problem of probabilistic output can be addressed by use of the Relevance Vector Machine (RVM). This is sparse Bayesian regression model, for which fast optimisation methods have already been developed. • It is also highlighted that for a probabilistic output to be meaningful, it is better to use data-driven, as opposed to model-based dictionaries, as this captures the covariance structure of original data sets better. • The motivation for this development lies in the compression of ultrasound signals, for engineering applications. Thus, the method is demonstrated, and illustrations are drawn from an ultrasound Non-Destructive-Test (NDT) data set. Abstract The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of random transformations to project high-dimensional measurements onto a much lower-dimensional domain. The second is the use of sparse regression to reconstruct the original signal. This assumes that a sparse representation exists for this signal in some known domain, manifested by a dictionary. The original formulation for CS specifies the use of an l 1 penalised regression method, the Lasso. Whilst this has worked well in literature, it suffers from two main drawbacks. First, the level of sparsity must be specified by the user, or tuned using sub-optimal approaches. Secondly, and most importantly, the Lasso is not probabilistic; it cannot quantify uncertainty in the signal reconstruction. This paper aims to address these two issues; it presents a framework for performing compressive sensing based on sparse Bayesian learning. Specifically, the proposed framework introduces the use of the Relevance Vector Machine (RVM), an established sparse kernel regression method, as the signal reconstruction step within the standard CS methodology. This framework is developed within the context of ultrasound signal processing in mind, and so examples and results of compression and reconstruction of ultrasound pulses are presented. The dictionary learning strategy is key to the successful application of any CS framework and even more so in the probabilistic setting used here. Therefore, a detailed discussion of this step is also included in the paper. The key contributions of this paper are a framework for a Bayesian approach to compressive sensing which is computationally efficient, alongside a discussion of uncertainty quantification in CS and different strategies for dictionary learning. The methods are demonstrated on an example dataset from collected from an aerospace composite panel. Being able to quantify uncertainty on signal reconstruction reveals that this grows as the level of compression increases. This is key when deciding appropriate compression levels, or whether to trust a reconstructed signal in applications of engineering and scientific interest. [ABSTRACT FROM AUTHOR]

The problem of how to accurately reconstruct the multicomponent signal from the ridges of the short-time Fourier transform (STFT) is considered. Especially when time-frequency representations contain strong noise corruption and crossed components, exact reconstruction becomes more difficult. In this paper, we propose two robust ridge reconstruction approaches, i.e., weighted ridge reconstruction (WRR) and self-paced ridge reconstruction (SPRR). The former is aim to select a more robust loss function to eliminate the influence of the strong noise and outliers. A half-quadratic optimization algorithm is developed to solve the proposed problem efficiently. The latter incorporates self-paced learning (SPL) method into ridge reconstruction model to sequentially include ridge points into signal reconstruction from easy to complex, which not only can suppress noise and outliers, but also can avoid a bad result in the presence of missing observations. Simulation and real-life signals are employed to show the effectiveness and practicability of the proposed approaches. [ABSTRACT FROM AUTHOR]

The combination of the complex structure and the nonstationary operating pattern makes the measured signal of the machinery a multi-component one with a complex time-varying spectral structure, challenging most time–frequency analysis methods in resolution, adaptability, and objectivity. As such, we propose a novel time–frequency analysis method, namely, the adaptive synchronous demodulation transform (ASDT), by adaptively designing a demodulation term according to the spectral structures of the target signals. The proposed demodulation term can make each component of the windowed signal stationary simultaneously without any prior knowledge, such as the number of components and the corresponding estimated instantaneous frequencies. ASDT possesses three main advantages: it can be used to analyze signals with nonlinear instantaneous frequencies; it can obtain the optimum frequency resolution when window width is set; and the demodulation term is flexible, implying that it can be specifically designed according to the frequency structures of different types of signals. Furthermore, ASDT allows for signal recovery. To testify the superiority of the proposed ASDT, two synthetic signals with parallel and proportional IFs structures were built, and several real-life signals, vibration signals collected from a planetary gearbox test rig and water turbine, and an echo signal collected from a brown bat were used. A comparison of the analysis results of ASDT with those of STFT, continuous wavelet transform, and Wigner–Ville distribution demonstrated the superiority of ASDT over conventional methods. [ABSTRACT FROM AUTHOR]

• Detect inner surface wall thinning by TM01 mode microwave with a vector network analyzer. • Establish a method to map frequency domain S-parameter to distance trace. • Detect and locate an abnormality from the distance trace. • Clarify type of discontinuity by spectral characteristic. • Find out characteristics features to determine the length and thickness of a pipe wall thinning. • Propose a methodology to estimate the utmost length of a pipe that can be inspected by means of the proposed technology. In microwave inspection of inner surface pipe wall thinning (PWT), the signals measured by a vector network analyzer (VNA) are usually single side-banded, complex-valued S-parameters in the frequency domain, whereas what we seek for, the location and dimension of a PWT, are real-valued quantities in spatial domain. In order to realize a more intuitive and direct detection and localization and avoid the intensive work of dispersion compensation, which is required in dealing with dispersive medium, we firstly transformed the S-parameters in the frequency domain into the wavenumber domain, and then to the spatial domain as a distance trace. A discontinuity along the pipe wall showed up as a distinct impulse in the distance trace, from which the location could be read out directly. Moreover, we applied gating to concerned responses and performed spectral analysis to reveal respectively spectral characteristics of flanged connection, pipe-end and PWT. We were thus able to classify PWT from other discontinuities. Spectral analysis of a PWT's gated responses also revealed that the spectrum's lob width is correlated to PWT length, whereas the lob's peak value is a measure of PWT thickness. We also investigated the utmost length of a pipe that can be explored by means of the proposed technology. The experimental results showed the feasibility of the proposed method and procedures for microwave pipeline inspection and monitoring. [ABSTRACT FROM AUTHOR]

• A novel multiple squeezing based on velocity synchronous chirplet transform (MSVSCT) is proposed. • The nonlinear basis transform and the time–frequency reassignment method are successfully combined, and good results have been achieved. • The proposed MSVSCT is able to handle synchronous multicomponent signals with closely-spaced frequencies, nonlinear instantaneous frequency (IF) trajectories, and noisy backgrounds. • The effectiveness of the proposed method has been successfully demonstrated by numerical and experimental examples. Traditional constant basis time–frequency analysis (TFA) methods are limited by the frequency smearing effect when dealing with time-varying synchronous multi-component signals, which makes it difficult to obtain a clear time–frequency representation (TFR) for signals with closely-spaced frequencies. Therefore, it is still a challenge to segregate the synchronous components and to concentrate the energy of frequency components in the TFR. As such, a new nonlinear basis TFA method is proposed in this paper, named multiple squeezing based on velocity synchronous chirplet transform (MSVSCT). In the MSVSCT, the second-order velocity-synchronous chirplet transform is used to generate a nonlinear basis that has a consistent trend complying the instantaneous frequency (IF) variations, so that synchronous components can be separated even in a closely spaced situation. The multi-squeezing technique is adopted to further improve the energy concentration and the accuracy of the TFR. Simulated and experimental signals are employed for validations. The analysis results confirm that MSVSCT has higher accuracy, more robustness to noise, better observability, and higher fidelity of signal reconstruction over other popular TFA methods when processing synchronous multi-component signals. [ABSTRACT FROM AUTHOR]

• Developed new generalized autoencoder (NGAE) for structural damage assessment. • Extracted power cepstral coefficients as damage sensitive features. • Statistical pattern recognition for damage detection and quantification. • Compared assessment accuracy of the NGAE against traditional autoencoder. Recently, the challenges in modeling complex dynamical systems, and the advancement in machine learning methodologies have indicated a new promising direction for damage assessment in civil and mechanical systems. Powerful and efficient data-driven approaches have been increasingly employed in Structural Health Monitoring (SHM) to extract Damage Sensitive Features (DSFs) from the monitored dynamic response of structures. In this study, a New Generalized Auto-Encoder (NGAE), integrated with a statistical-pattern-recognition-based approach that uses the power cepstral coefficients of structural acceleration responses as DSFs, is proposed for structural damage assessment. This NGAE is well-generalized in the components of cepstral coefficients that represent the structural properties of the entire system thanks to a newly defined encoder-decoder mapping, which largely reduces rid of the data variance attributed to different types of excitations and measurement noise. The cepstral coefficients, by virtue of a compact representation of the structural properties, can greatly simplify the structure of the NGAE, and therefore, significantly accelerate training and inference speeds with very few computational requirements. Two specific evaluation metrics that relates to the autoencoder signal reconstruction error are defined and used to assess the presence of damage. The proposed method has been validated through numerical simulations and experimental data, and shows better performance compared to a Traditional Auto-Encoder (TAE) and the Principal Component Analysis (PCA). [ABSTRACT FROM AUTHOR]

Yaw rate is a crucial signal for the motion control systems of ground vehicles. Yet it may be contaminated by sensor bias. In order to correct the contaminated yaw rate signal and estimate the sensor bias, a robust gain-scheduling observer is proposed in this paper. First of all, a two-degree-of-freedom (2DOF) vehicle lateral and yaw dynamic model is presented, and then a Luenberger-like observer is proposed. To make the observer more applicable to real vehicle driving operations, a 2DOF vehicle model with uncertainties on the coefficients of tire cornering stiffness is employed. Further, a gain-scheduling approach and a robustness enhancement are introduced, leading to a robust gain-scheduling observer. Sensor bias detection mechanism is also designed. Case studies are conducted using an electric ground vehicle to assess the performance of signal correction and sensor bias estimation under difference scenarios. [ABSTRACT FROM AUTHOR]

To handle problems of accurate signal reconstruction and controller implementation with integral and derivative components in the presence of noisy measurement, motivated by the design principle of sigmoid function based tracking differentiator and nonlinear continuous integral-derivative observer, a novel integral-derivative observer (SIDO) using sigmoid function is developed. The key merit of the proposed SIDO is that it can simultaneously provide continuous integral and differential estimates with almost no drift phenomena and chattering effect, as well as acceptable noise-tolerance performance from output measurement, and the stability is established based on exponential stability and singular perturbation theory. In addition, the effectiveness of SIDO in suppressing drift phenomena and high frequency noises is firstly revealed using describing function and confirmed through simulation comparisons. Finally, the theoretical results on SIDO are demonstrated with application to autopilot design: 1) the integral and tracking estimates are extracted from the sensed pitch angular rate contaminated by nonwhite noises in feedback loop, 2) the PID(proportional-integral-derivative) based attitude controller is realized by adopting the error estimates offered by SIDO instead of using the ideal integral and derivative operator to achieve satisfactory tracking performance under control constraint. [ABSTRACT FROM AUTHOR]

• An enhanced method for reducing reconstruction error of the CS is proposed. • Prior information is provided by the time series feature of vibration signal. • The optimization problem is formulated to decrease the residuum of the AR model. • The compressed measurement is transformed as the general solution of nonhomogeneous linear equation. Compressed sensing (CS) utilizes the signal's sparsity to reconstruct signals from far less linear measurements. However, ambient vibration response in structural dynamics typically lacks sparsity on a regular transform basis. Hence, when the vibration signals are reconstructed through the CS, significant errors are unavoidably induced, especially at high compression ratios, limiting the CS applicability in structural health monitoring and damage detection. To address these issues, this paper proposes an enhanced error reduction method, exploited as a post-processing scheme for signal reconstruction. The suggested method constructs an autoregression model, whose residuum increases to correspond to the reconstruction error based on empirical observations. Through minimizing the residuum under the constraint of compressed measurements, the reconstruction error is then minimized, which leads to an optimized result of the reconstructed signals. The suggested method is validated using ambient vibration data collected from a laboratory-scale shear frame model and a full-scale cable-stayed bridge. [ABSTRACT FROM AUTHOR]

Severe blade vibrations may reduce the useful life of the high-speed blade. Nowadays, non-contact measurement using blade tip-timing (BTT) technology is becoming promising in blade vibration monitoring. However, blade tip-timing signals are typically under-sampled. How to extract characteristic features of unknown multi-mode blade vibrations by analyzing these under-sampled signals becomes a big challenge. In this paper, a novel BTT analysis method for reconstructing unknown multi-mode blade vibration signals is proposed. The method consists of two key steps. First, a sparse representation (SR) mathematical model for sparse blade tip-timing signals is built. Second, a multi-mode blade vibration reconstruction algorithm is proposed to solve this SR problem. Experiments are carried out to validate the feasibility of the proposed method. The main advantage of this method is its ability to reconstruct unknown multi-mode blade vibration signals with high accuracy. The minimal requirements of probe number are also presented to provide guidelines for BTT system design. [ABSTRACT FROM AUTHOR]

In this paper, a novel transient signal reconstruction method, called time–frequency manifold (TFM) sparse reconstruction, is proposed for bearing fault feature extraction. This method introduces image sparse reconstruction into the TFM analysis framework. According to the excellent denoising performance of TFM, a more effective time–frequency (TF) dictionary can be learned from the TFM signature by image sparse decomposition based on orthogonal matching pursuit (OMP). Then, the TF distribution (TFD) of the raw signal in a reconstructed phase space would be re-expressed with the sum of learned TF atoms multiplied by corresponding coefficients. Finally, one-dimensional signal can be achieved again by the inverse process of TF analysis (TFA). Meanwhile, the amplitude information of the raw signal would be well reconstructed. The proposed technique combines the merits of the TFM in denoising and the atomic decomposition in image sparse reconstruction. Moreover, the combination makes it possible to express the nonlinear signal processing results explicitly in theory. The effectiveness of the proposed TFM sparse reconstruction method is verified by experimental analysis for bearing fault feature extraction. [ABSTRACT FROM AUTHOR]

• An entropy matching chirplet transform (EMCT) is proposed. • A novel EMSSCT method is proposed to generate a concentrated time-frequency representation. • EMSSCT can be effectively applied to strongly time-varying signals with different modulated frequencies. Time-frequency (TF) analysis (TFA) is an effective tool to characterize non-stationary features of time-varying signals, which has been developing towards the goal of higher energy concentration and better reconstruction performance in recent years. However, it is still a challenging task to analyze the multi-component signal with strong and distinct modulated frequencies. We put forward a novel TFA method based on chirplet transform to deal with this problem. This paper first optimizes the matching chirplet transform by measuring Rényi entropy to generate a more accurate TF result. Given the sharpness based on accuracy, this paper further studies an unbiased IF estimation that plays a crucial role in the reassignment of inevitable diffusion energy. In this work, we provide the theoretical analysis of the novel TFA method to show that this is an upgrade and extension of classical chirplet transform and synchrosqueezing transform. Moreover, we prove in theory that the proposed method retains perfect signal reconstruction ability. Numerical and experimental signals are analyzed, and the results validate that this method has a better performance in representing a wide variety of multi-component signals with complex modulation features than some advanced TFA methods. [ABSTRACT FROM AUTHOR]

• A novel dispersive decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) for structural health monitoring. • Frequency envelope tracking with joint-estimation technique to accurately extract the overlapped dispersive components. • Non-parameterized group delay (GD) refinement procedure to ameliorate the time–frequency resolution. • Adaptive ridge detection technique to automatically estimate the number of components and their GDs with less prior knowledge. • Comprehensive comparison among five related time–frequency analysis methods. Dispersive signals are omnipresent in structural health monitoring and nondestructive testing. Due to the strong dispersion and multimodal characteristics, such signals often contain multiple components with frequency-varying group delays (GDs) overlapping in time–frequency (TF) domain, bringing great challenges to the existing signal processing methods. This paper presents a novel dispersive signal decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) to accurately estimate the dispersion curves and separate overlapped components. Specifically, an envelope tracking issue is introduced in frequency domain based on dispersive signal model. Then, GDs are refined by recovered frequency envelope, and an iterative processing procedure is designed to ameliorate the TF resolution. To initialize the IFETF, an adaptive ridge detection technique is introduced to automatically estimate the number of components and their GDs with little prior knowledge. In addition, a high-quality TF representation can be constructed according to the results of IFETF, unambiguously revealing TF patterns of a multi-component dispersive signal. The proposed method was demonstrated by numerical analysis and engineering applications on broadband Lamb wave inspection and rail damage detection. Compared with traditional TF analysis techniques, the results show that the IFETF provides higher accuracy and efficiency in signal reconstruction and GD estimation. [ABSTRACT FROM AUTHOR]

Time–frequency (TF) analysis (TFA) method is an effective tool to characterize the time-varying feature of a signal, which has drawn many attentions in a fairly long period. With the development of TFA, many advanced methods are proposed, which can provide more precise TF results. However, some restrictions are introduced inevitably. In this paper, we introduce a novel TFA method, termed as general linear chirplet transform (GLCT), which can overcome some limitations existed in current TFA methods. In numerical and experimental validations, by comparing with current TFA methods, some advantages of GLCT are demonstrated, which consist of well-characterizing the signal of multi-component with distinct non-linear features, being independent to the mathematical model and initial TFA method, allowing for the reconstruction of the interested component, and being non-sensitivity to noise. [ABSTRACT FROM AUTHOR]

In this work, we propose a modification of the traditional Auto Associative Kernel Regression (AAKR) method which enhances the signal reconstruction robustness, i.e., the capability of reconstructing abnormal signals to the values expected in normal conditions. The modification is based on the definition of a new procedure for the computation of the similarity between the present measurements and the historical patterns used to perform the signal reconstructions. The underlying conjecture for this is that malfunctions causing variations of a small number of signals are more frequent than those causing variations of a large number of signals. The proposed method has been applied to real normal condition data collected in an industrial plant for energy production. Its performance has been verified considering synthetic and real malfunctioning. The obtained results show an improvement in the early detection of abnormal conditions and the correct identification of the signals responsible of triggering the detection. [ABSTRACT FROM AUTHOR]

In this paper, a novel time–frequency analysis (TFA) technique termed High-order Synchroextracting Transform (HSET), is proposed to better characterize the changing dynamics of multi-component signals with strong AM-FM components. Synchroextracting Transform (SET) is a TFA method has been developed recently. As a post-processing technology, unlike the parameterized TFA methods that requires different demodulation operators to be designed according to different types of signals, SET can generate high-quality time–frequency representation (TFR) only on the basis of short-time Fourier transform (STFT). However, since the core of SET is based on the assumption that the signal is composed of pure harmonics, SET cannot accurately extract the time-varying characteristics of signal with strong AM-FM components. To construct a method to well characterize the time-varying laws of strong AM-FM signals, this paper uses high-order Taylor expansion to estimate the time varying laws of the signal to obtain a more accurate frequency estimation operator, which we call high-order Synchroextracting operator (HSEO), and then HSET can be constructed through Dirichlet function to generate TFR with high TF resolution. Good reconstruction quality can be achieved through the inverse transformation of HSET while signal reconstruction operation can be completed concisely. In addition, this paper applies HSET to the processing of the measured vibration signals and feature extraction of gravitational-wave (GW) signal. Compared with other advanced TFA technologies, the effectiveness and competitiveness of the proposed method can be demonstrated. [ABSTRACT FROM AUTHOR]

• The compression sampling and reconstruction of road roughness signal were realized. • The proposed ISA-SPA reconstruction method can adaptively match sparsity. • SA-GSM-SPA reconstruction method matched sparsity accurately and consumed less time. • The method presented in the paper was validated on hard pavement and soft pavement. • Sparsity matching equaled to extremal process of convex function approximately. To significantly reduce the storage space of collected road roughness signals and improve the collection rate, the work investigates the compressive sampling and reconstruction of road roughness signals based on compressive sensing theory. Moreover, to overcome the limitations of the classical signal reconstruction method in the case of unknown sparsity, two sparsity adaptive compressive signal reconstruction methods namely those based on the improved simulated annealing (I-SA) algorithm and the golden section method (GSM) are respectively proposed and compared. Both simulated and measured road roughness signals are used to verify the validity of the novel reconstruction methods and the feasibility of road roughness compression and collection. The research results show that the proposed ISA-SPA method (the sparsity adaptive reconstruction method based on the I-SA) completes the sparsity matching optimization with high reconstruction precision (R 2 = 0.9884), but has a high time consumption (about 16.09 s). Moreover, the proposed SA-GSM-SPA method (the sparsity adaptive reconstruction method based on the GSM) has a fast rate of calculation while inheriting the good sparsity estimation result and high reconstruction precision of the ISA-SPA method. [ABSTRACT FROM AUTHOR]

The onset of the Internet of Things enables machines to be outfitted with always-on sensors that can provide health information to cloud-based monitoring systems for prognostics and health management (PHM), which greatly improves reliability and avoids downtime of machines and processes on the shop floor. On the other hand, real-time monitoring produces large amounts of data, leading to significant challenges for efficient and effective data transmission (from the shop floor to the cloud) and analysis (in the cloud). Restricted by industrial hardware capability, especially Internet bandwidth, most solutions approach data transmission from the perspective of data compression (before transmission, at local computing devices) coupled with data reconstruction (after transmission, in the cloud). However, existing data compression techniques may not adapt to domain-specific characteristics of data, and hence have limitations in addressing high compression ratios where full restoration of signal details is important for revealing machine conditions. This study integrates Deep Convolutional Autoencoders (DCAE) with local structure and physics-informed loss terms that incorporate PHM domain knowledge such as the importance of frequency content for machine fault diagnosis. Furthermore, Fault Division Autoencoder Multiplexing (FDAM) is proposed to mitigate the negative effects of multiple disjoint operating conditions on reconstruction fidelity. The proposed methods are evaluated on two case studies, and autocorrelation-based noise analysis provides insight into the relative performance across machine health and operating conditions. Results indicate that physically-informed DCAE compression outperforms prevalent data compression approaches, such as compressed sensing, Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and DCAE with a standard loss function. FDAM can further improve the data reconstruction quality for certain machine conditions. • Deep learning-based data compression demonstrated for benefitting Big Data condition monitoring pipelines. • Physics-informed methods developed to improve deep neural networks-based signal compression. • Separate encoders applied for disjoint faulty signal compression to increase reconstruction accuracy. • Noise estimation techniques proposed to explain model performance in light of signal features. [ABSTRACT FROM AUTHOR]

Abstract: A novel approach for the compression of mechanical vibration signals is presented in this paper. The method relies on a simple and flexible decomposition into a large number of subbands, implemented by an orthogonal transform. Compression is achieved by a uniform adaptive quantization of each subband. The method is tested on a large number of real vibration signals issued by plane engines. High compression ratios can be achieved, while keeping a good quality of the reconstructed signal. It is also shown that compression has little impact on the detection of some commonly encountered defects of the plane engine. [Copyright &y& Elsevier]

Abstract: In wavelet analysis, the signal reconstruction and statistical estimators are strongly influenced by the wavelet shape that controls the time-frequency localization properties. However, this dependency does not imply that the wavelet form is physically relevant to extract spatially invariant wavelet-based shape signatures. A new statistical estimator is proposed in order to quantify the influence of the wavelet on the spatial detection. This indicator is then used to analyze signals obtained from the topography of abraded surfaces. It is shown that the Coiflet wavelet is physically adapted to reproduce the elementary mechanical process which creates the abrasion of the surface. Using 8 different wavelets, the analysis of the signal obtained by scanning the abraded surface leads to the same spatial localization regardless the parameters of the abrasive process and whatever the wavelet shapes. No statistical difference related to the type of wavelets is found between the indicators (RMS, spectral moments…) extracted from the reconstructed signals calculated on different scales and for various abrasive processes. If the wavelet decomposition is seen as a multiscale microscope, a surface can be seen in different ways according to the type of wavelets. However, the morphological changes of the surface caused by external mechanical causes and characterized by several statistical parameters are statistically similar regardless the shape of the wavelets. [Copyright &y& Elsevier]

• A mathematical model of BTT signals based on the MUSIC algorithm is established. • The unambiguous reconstruction conditions of BTT signals are proposed. • The spectrum aliasing in the reconstruction of BTT signals is overcome. • An amplitude identification method based on DFT and remainder theorem is proposed. As a multi-frequency identification method, the MUSIC method has received more and more attention in the field of blade tip-timing (BTT) signal reconstruction. So far, an improved MUSIC method and a subspace dimension-reduced MUSIC (SDR-MUSIC) method were proposed to analyze BTT signals. However, they must rely on the expanded snapshot matrix to overcome the spectrum aliasing. The computational complexity of these methods was analyzed. The computation time of BTT signal reconstruction increased cubically as the size of the expanded matrix increased. This is unfavorable for real-time processing. In this paper, reconstruction conditions based on the MUSIC algorithm were proposed. This makes the traditional MUSIC method overcome spectrum aliasing without expanding the snapshot matrix. The computational complexity is greatly reduced. The feasibility of the reconstruction conditions was verified through simulations and aero-engine experiments. Under the reconstruction conditions, the frequency identification accuracy of the traditional MUSIC can reach that of the improved MUSIC method. Besides, an amplitude identification method was proposed based on the discrete Fourier transformation (DFT) and remainder theorem. It provides a feasible solution for the spectrum methods that cannot directly extract amplitude from the pseudo spectrum, such as the improved MUSIC, SDR-MUSIC, etc. The error of the amplitude identification was analyzed by simulations. And its feasibility was further verified by identifying all blade vibration amplitudes in the aero-engine experiments. [ABSTRACT FROM AUTHOR]

• A novel adaptive nonlinear ANC system based on time-domain signal reconstruction technology is proposed. • The noise signal is decomposed to reduce the degree of non-stationarity. • A multi-network reconstruction model is designed for reconstructing the noise signal of passenger ear-sides. • Results demonstrate that proposed ANC system can realise the fault-tolerant and robustness control. When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the fault-tolerate and robustness of the system, the study proposes an adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size LMS (VSS-LMS) algorithm. The noise signal decomposition method is used to address the non-stationary of the interior noise; Based on the signal components, the multi-network model for the noise signal reconstruction of passenger ear-sides is designed, which is pre-trained by a restricted Boltzmann machine for improved reconstruction accuracy and realize the adaptive extraction of signal features; And then based on the reconstruction signal components, the controller weights of corresponding components are adaptively updated by the VSS-LMS algorithm to control the passenger ear-sides noise. The effectiveness of the proposed adaptive nonlinear ANC system is validated using noise signal sources collected from a vehicle. Compared with the different ANC systems, the proposed system is superior in terms of fault-tolerant and robustness, which can guarantee stable work of the interior noise control. [ABSTRACT FROM AUTHOR]

• A signal can be decomposed and reconstructed by group filters by DCWT in time domain. • The decomposition filters of DCWT can be chosen freely in time domain. • The reconstruction filters group can be obtained by RC equation in frequency domain. • By correlation analysis, optimized filters are solved to get main features of signal. • Decomposition and reconstruction based on convolution method is easier to implement. • The computation cost of FMPA is very low. Thus, FMPA can be used in large-scale data. This paper introduces a new kind of signal decomposition and reconstruction method called Discrete Convolution Wavelet Transform (DCWT), and it is used to analyze the accident pattern data of battery electric vehicles (BEV). Normally, some feature signals directly related to accidents can be obtained from the BEV daily monitoring system, but how can we match pursuit those similar feature signals when BEV is running? The DCWT method is proposed from the Frequency Slice Wavelet Transform (FSWT) defined in frequency-domain, but DCWT is defined in time-domain by convolution filters. Though the original signal can be easily decomposed and reconstructed by FSWT, it is difficult to use in large-scale and real-time computation. At first, a simple signal Decomposition & Reconstruction Technical Framework (DRTF) is presented. In order to reconstruct the original signal completely, it is important to discuss the reconstruction condition (RC) of DCWT and the filter selection methods. By means of the correlation analysis, an filter optimization algorithm is designed to obtain the main features of pattern signals. Finally, a feature matching pursuit algorithm based on DCWT is proposed to find the accident feature in a real BEV accident data. Summarily, this paper presents a new convolution wavelet transform method, in which the original signal can be decomposed and reconstructed by two groups of filters. The decomposition filters can be designed as need and the reconstruction filters can also be obtained by RC equation, and both of them can be easily optimized in practice. By comparing analysis, DCWT method can fast decompose signal to obtain its feature signals. Some conclusions are drawn that the DCWT method is practical and will become a new idea of signal decomposing and signal identifying. [ABSTRACT FROM AUTHOR]

• The relationships of the reflected signal and the incident signal are clarified. • The spectrum of the incident signal is obtained from the reflected signals. • A reconstruction algorithm of the incident signal is proposed. The oil film thickness in an oil-lubricated tribo-pair can be measured using reflected ultrasonic waves and various transformation models. Conventionally, this approach requires the incident signal to be calibrated using off-line methods prior to testing, and this limits the on-line measurement of oil film thickness in sliding components such as journal bearings. To enable on-line calibration, we propose a method of reconstructing the incident signal from the easily obtained reflected signal. This involves analyzing the amplitude and phase spectrum of the reflection coefficient to reconstruct the incident signal. More specifically, the following main findings of the amplitude and phase of reflection coefficient at the resonance frequency are reported. 1) There is a zero-crossing at the resonance frequency, and therefore the phases of the incident and reflected signal are the same. 2) If part of the reflected signal is included for calculation, the frequency of the extreme point in the amplitude spectrum of the reflected signal is equal to the resonance frequency only when the extreme point phenomenon occurs at the center frequency of the transducer. At other positions, the frequency of the minimum amplitude in the amplitude spectrum of the reflected signal is not equal to the resonance frequency. Utilizing these relationships, the phase and amplitude of the incident signal can be accurately reconstructed by following the proposed method, provided the thickness of the oil film between the tribo-pairs can be widely ranged to cover both the blind and the resonance zones. Correspondingly, a practical schedule for the on-line reconstruction of the incident signal is proposed, which includes an operation to adjust the oil film thickness within the blind and the resonance zone. This method is validated on a test rig by comparing it with the traditional off-line methods. [ABSTRACT FROM AUTHOR]

• Improve compressive sensing used for crack segmentation in practical applications. • Replace sparsity constraint in compressive sensing by deep generative models. • Reduce size of training data with imperfect generative model capturing cracks well. • Achieve high segmentation accuracy and robustness to noise with the proposed method. In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy-efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. Different from the popular approach of simultaneously training encoder and decoder using neural network models, the CS theory ensures a high probability of accurate signal reconstruction based on random measurements that is shorter than the length of the original signal under a sparsity constraint. Such method is particularly useful when measurements are expensive, such as wireless sensing of civil structures, because its hardware implementation allows down sampling of signals during the sensing process. Hence, CS methods can achieve significant energy saving for the sensing devices. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard to guarantee for many real images, such as image of cracks. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method. We demonstrate the remarkable performance of our method that takes advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparisons to three existing CS algorithms. Furthermore, we show that our framework is potentially extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion. [ABSTRACT FROM AUTHOR]

• We proposed a new spectral signal reconstruction method for a multispectral imaging capturing system with nine spectral bands. • We design a new filter array in a 4×4 MSFA pattern and propose a multispectral demosaicking algorithm for recovering the sparse spectral data. • The experimental results demonstrate that our proposed demosaicking algorithm outperforms the other methods in PSNR, SSIM, and subjective evaluation. Multispectral filter array (MSFA) imaging with one single sensor is a portable and inexpensive means of acquiring spectral image which is widely used for object detection, material analysis and mechanical system diagnosis. The most challenging task for MSFA imaging is the multispectral demosaicking with the aim of reconstructing the captured raw/mosaic image, especially for the systems with many bands which result in higher sparseness of the raw data. In this paper, we present a 9-band MSFA imaging system in a repetitive 4 × 4 filter array on a single sensor, and propose a demosaicking algorithm for reconstructing the raw spectral image. Within the 4 × 4 MSFA pattern, the fifth spectral band takes up half of the total spatial position while the remaining eight bands occupy 1/16 respectively. To reconstruct the sparse raw data, we first recover the fifth band by propagating the neighboring sampled pixels to the unsampled position using the image gradients, and then employ the reconstructed fifth band as a guided image to demosaick the other bands with the guided filter and residual interpolation. Finally, we estimate the spectral reflectance values from the multispectral image and the characterization matrix. In the experiment, we evaluate the performance of the 9-band imaging system with the binary tree-based edge-sensing (BTES) algorithm, compressed sensing (CS) algorithm, and our proposed demosaicking algorithm. The experiment results demonstrate that our demosaicking algorithm not only outperforms BTES and CS algorithms in terms of objective image quality, e.g., PSNR values and spectral errors, but also reduces the demosaicking artifacts in terms of subjective evaluations. [ABSTRACT FROM AUTHOR]

• A novel a Multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm is proposed. • The SCS method adaptively extracts effective features of noise signals. • A new DBN-NN model is established for reconstructing the noise signal of passenger ear-sides. • The RCS method is used to recover the time-domain signal. • The proposed reconstruction method can realise the rapid signal reconstruction of passenger ear-sides. During vehicle driving, the noise signal of passenger ear-sides is affected by many sound sources. Meanwhile, the composition and generation mechanism of these sound sources are complicated. The application of traditional data-driven technology in the noise signal reconstruction process of passenger ear-sides often requires complex signal processing and prior knowledge, thereby limiting its application in signal reconstruction. Thus, a multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm based on compressed sensing and deep learning is proposed to address such limitation. Raw data are compressed to acquire samples using the proposed algorithm to reduce the amount of data and realize the adaptive extraction of signal features. A deep neural network model for the noise signal reconstruction of passenger ear-sides is established on the basis of multisource signals of the compressed domain, which is pretrained by a restricted Boltzmann machine for improved reconstruction accuracy. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger ear-sides. The effectiveness of the proposed MTSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the different reconstruction models, the proposed algorithm is superior in reconstruction accuracy and time consumption. [ABSTRACT FROM AUTHOR]

• An OMP-based signal compression and reconstruction approach was proposed. • The OMP algorithm was modified and implemented to the impedance based technique. • Proof of experiment on a simply-supported steel beam mounted with PZT was conducted. • Practical validation of the approach was performed on the PZT-based concrete tunnel SHM. • Effectiveness of the approach was analyzed through qualitative and quantitative comparisons. Signal compression and reconstruction were critical for damage detection in engineering structural health monitoring (SHM), on account of large amounts of sensor data collected and processed in signal acquisition system. A signal compression and recovery approach for damage detection using piezoelectric ceramic transducer (PZT) in structural monitoring system was proposed in this article. The basis of this approach was to first perform a linear projection of the transmitted data x in the monitoring system onto y by a random matrix and subsequently to feedback the data y to the receiving system. An algorithm of sparsity-adaptive Orthogonal Matching Pursuit (OMP) modified via optimal parameter analysis was explored to improve both the recovery effect and the compression ratio (CR) of compressed sensing (CS) in data processing stage. A statistical index was then introduced to identify the vector characteristics. The proposed method was sufficiently validated with the electromechanical admittance (EMA) data collected in an experiment for local damage detection on a simply-supported steel beam, and further applied to a long-time health monitoring of full-scaled shield tunnel segment structure. Qualitative and quantitative comparisons between the reconstructed and the original signals in structural damage detection under multiple conditions indicated that the proposed compression and recovery approach was of high accuracy and robustness to immune from the sensor conditions and temperature/environment impact, thus providing promising assistance to the impedance/admittance based SHM practice. [ABSTRACT FROM AUTHOR]