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The lack of labelled data presents a common challenge in many fault diagnosis and machine learning tasks. It requires the model to be able to efficiently capture useful fault features from a smaller amount of labelled data. In this paper, a method to train multiple convolutional auto‐encoders by self‐learning method and integrate them using ensemble learning, called ensemble self‐taught learning convolutional auto‐encoders (STL‐CAEs), is proposed, which can effectively extract features of bearing vibration signals. First, an ensemble learning strategy is proposed to obtain two auto‐encoders that satisfy the strategy by optimizing the model parameters and structure. Then, a self‐taught learning training method is proposed to solve the problem of little label data. Finally, ensemble learning and fault diagnosis is achieved by the SoftMax classifier. Applying the proposed method to the bearing data from Case Western Reserve University, the STL‐CAEs have higher accuracy and generalization than common fault diagnosis methods such as CAE, CNN, SAE and EMD, and also have significant advantages in terms of diagnostic time and training time. [ABSTRACT FROM AUTHOR]

A rapid and accurate method is presented to determine CaCO3, SiO2, Fe2O3, and Al2O3 in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO3, SiO2, Fe2O3, and Al2O3. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer. [ABSTRACT FROM AUTHOR]

It is highly probable for the structures of carbon fiber reinforced plastics (CFRP) to suffer from invisible impact damage, for which the identification of such damage is of great significance. For the purpose of damage localization, this paper proposes a minimum variance distortion-less response (MVDR) algorithm that employs linear array sensor. In addition, a near field signal model based on active Lamb wave is established and the propagation of Lamb wave is investigated in composite laminates through the method of simulation. Moreover, a damage imaging system is also set up for composites to verify the effectiveness of the MVDR algorithm. Besides, in the study, wavelet transform is used as well to extract narrowband signals and promote signal-to-noise. It is shown by the results that the proposed algorithm can accurately identify the location of damage through the method of imaging. It is concluded that the maximum error of damage identification is 1.2 cm and MVDR algorithm has great potential in the field, damage imaging of composite materials. As a result, it provides a novel idea for damage imaging of composite materials. [ABSTRACT FROM AUTHOR]

State monitoring and fault diagnosis of an internal combustion engine are critical for complex machinery safety. In the present study, a high-frequency vibration system was proposed based on Fiber Bragg Grating (FBG) cantilever sensor and intelligent algorithm. Structural vibration signal containing fault information of engine valves and oil nozzle was identified by FBG sensors and preprocessed using wavelet decomposition and reconstruction. Moreover, vibration energy was taken as fault characteristics. Subsequently, a fault identification model was built based on multiclass υ-support vector classification (υ-SVC). Experimental tests on the valve fault and fuel injection advance angle fault were performed and presented to verify the efficacy of the proposed approach. The results here reveal that the proposed method exhibits excellent fault detection performance for ICE fault identification. Furthermore, the proposed method can achieve higher performance than other methods in the fault identification accuracy. [ABSTRACT FROM AUTHOR]

• A novel cross-domain fault diagnosis method for the rolling bearing is proposed. • The designed Adaptive threshold network as the backbone network of ATN-MAML. • The improved threshold function can effectively filter irrelevant information. • The model can obtain high accuracy even facing huge domain differences under limited data. Fault diagnosis of rotating machinery based on deep learning requires many identical labeled data to achieve satisfactory results. However, in engineering scenarios, few typical fault data have been manually marked, resulting in a poor on-site diagnosis consequence. Thus, training an efficient cross-domain diagnosis model under limited data is extremely challenging. This paper proposes a novel cross-domain fault diagnosis method based on model-agnostic meta -learning embedded in Adaptive threshold network (ATN-MAML). The threshold acquisition module is built to determine the signal's irrelevant information threshold adaptively. The designed threshold function is embedded in the network to filter out invalid information effectively as a non-linear layer. Then, MAML's training strategy is optimized to prevent overfitting. Finally, many cross-domain comparison experiments are conducted to verify the effectiveness of the proposed method on two datasets. The results show the model can maintain high accuracy even when the source and target domain data are from different devices. [ABSTRACT FROM AUTHOR]

The current deep learning-based fault diagnosis methods have achieved excellent classification performance under supervised training, but the existence of domain discrepancy under different working conditions will greatly affect data relationship mining and fault diagnosis accuracy. Graph convolutional network (GCN) with topological structured relationships is excellent in solving these issues. In this paper, structural discrepancy and domain adversarial fusion network (SDDAFN) is proposed to learn deep high-dimensional features and eliminate distribution discrepancy across different domains. In data structure construction, the high-dimensional mapping features are first extracted accurately from the input raw signals by a feature encoder, then the output of encoder is converted into an instance graph through graph construction layer (GCL) for the convenient modeling by multi-channel kernel GCN (MCK-GCN). In network optimization, the classification loss function is introduced to optimize the discrimination boundaries of different fault types, and the integrated loss function based on structural discrepancy and domain adversarial are jointly adopted to minimize the domain distribution discrepancy. Experimental cases show the proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Nonlinear acoustic mechanisms are observed in gas-liquid-solid coupling interfaces. • Capillary pressure of liquid filled in cracks is conducive to elastic nonlinearity. • Viscous pressure increases hysteresis and dissipation, which contribute to sidebands. • Swept signal is applied to trigger nonlinear behaviors without reference data. • The nonlinear acoustic detection for breaking cracks filled with liquid is achieved. Supervised strategies for ultrasonic-based cracks detection in existing structures has been one of particular concerns in the engineering community. However, current researches mostly focus on the clean microcracks, while ignoring the fact that such cracks will be contaminated with liquid impurities (lubricants, silicone oil, etc.) due to its exposure to the operational environment. Actually, the presence of liquid may lead to a non-negligible change in the acoustic performance and interfere with damage diagnosis. The central contribution of this work is to reveal the nonlinear acoustic mechanisms and phenomena induced by the gas–liquid–solid coupling interfaces in such liquid-filled cracks and investigate their potential applications for such crack detection. Firstly, the state equation for a liquid-filled cavity in beams is established to determine the nonlinear dependences of stress–strain relationship at crack wall on the viscous and capillary pressure in liquid. Secondly, a modified acoustic modulation method, in which swept signal is used as the pumping wave to sufficiently trigger and enhance the nonlinear behaviours at the gas–liquid–solid interfaces without requiring exact reference information, is utilized for detecting sub-millimeter cracks filled with different liquid. The results suggest that the liquid fillers may cause considerable increase of the damage-related acoustic nonlinearities involving hysteresis nonlinearity, nonlinear elastic behavior and dissipative nonlinearity. Additional analysis provides compelling evidence for the following valuable conclusions: 1) the capillary pressure is a key factor that promotes conducive to the nonlinear elastic behavior that is the primary source of the first-order sum-difference frequency; 2) the viscous pressure contributes to hysteresis as well as the nonlinear dissipation, and the latter promotes the second-order sum frequency. Further, such nonlinear performance especially dissipation offer promising prospects for nonlinear acoustic diagnostic of such cracks. All results obtained in this study will aid in understanding the nonlinear wave processes and further improving the reliability of crack detection in engineering. [ABSTRACT FROM AUTHOR]

• Proposed a modified RAPID based on damaged virtual sensing paths (DVSPs). • Proposed two methods for finding DVSPs based on time of flight and damage index. • Built aluminum and composite laminate systems to verify the adaptability of the algorithm. • Verified the effectiveness of the algorithm using simulated and impact damage. Ultrasonic guided wave is an effective approach for structural damage detection, among which the reconstruction algorithm for probabilistic inspection of damage (RAPID) is widely used. For the problem that the localization accuracy of traditional RAPID is seriously affected by the damage shape factor β , this paper proposes a modified RAPID based on damaged virtual sensing paths (DVSPs). The DVSP is determined by the time of flight (TOF) or damage index (DI) and is defined as a line of passing damage from a real actuator to a virtual sensor. Using DVSPs instead of real paths for damage localization can effectively avoid the β selection dilemma. Experiments were conducted on an aluminum plate and composite laminates. With the change of β , the average localization error does not exceed 9 mm, and the variation is less than 2 mm, while the error variance of the traditional RAPID is more than 35 mm. [ABSTRACT FROM AUTHOR]

We discuss the processes involved in the formation of a few monolayer thick pentacene films on the SiO 2 surface. The evolution of the optical properties has been monitored in situ by optical spectroscopy and the interface morphology was investigated ex situ by atomic force microscopy. The sub monolayer shows the identical characteristic optical response with the intensity proportional to the coverage. From the second monolayer growth on, the optical properties are significantly changed. This experimental evidence indicates the formation of the first monolayer layer comprising the so-called “orthorhombic phase” crystallites with near-vertically oriented pentacene molecules, and the multilayer above comprises the so-called “thin-film phase” crystallites. Most importantly, the combination of optical spectroscopy and atomic force microscopy (AFM) investigations reveals that the first monolayer crystallize into the orthorhombic phase and the structure is persevered even after being buried by the multilayer. These observations suggest that the thickness-driven phase transformation from the orthorhombic phase to the thin-film phase starts from the second monolayer on rather than the first monolayer. This result provides a new insight into the structural evolutions and growth of pentacene thin films on SiO 2 . [ABSTRACT FROM AUTHOR]

Sensing characteristics of FBG sensor embedded in carbon fiber reinforced polymer (CFRP) laminate were experimentally investigated in this work. In order to evaluate the monitoring accuracy, two upper/lower surface bonded FBG sensors were also included as the measurement references. Results reveal that the wavelength of embedded FBG changes sensitively corresponding to the temperature or strain variation during the experiments: responses to temperature and static loads follow highly linear trend, showing a good agreement with that of surface bonded FBGs, while the frequency responses to low velocity impact give the same resonant frequency but lower amplitudes compared with the ones of surface bonded FBGs. This work confirms that embedded FBG sensor can effectively and reliably monitor the inner conditions of composite materials. Due to the advantages of precise measurement, small size, easy integration and so on, the embedded FBG sensors have broad application prospects in the field of intelligent composites. [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]

• The proposed methodology is based on the improved SGMD and optimized ELM. • CDF, PCC and VEWM constrains are adopted to reconstruct SGCs to improve SGMD. • The feature mapping method by PSE weighted singular values can more efficiently extract fault features. • ACO is introduced to iteratively optimize the input weights and bias in ELM. • The performance of the proposed method is validated by experiments. The signal of rotating machinery is usually non-stationary, non-linear, and with noise interference. The early fault signal is too weak to extract fault features and the accuracy cannot be effectively guaranteed. A novel fault diagnosis method based on adaptive weighted symplectic geometry mode decomposition (AWSGMD-CP) with Cosine difference factor (CDF) and Pearson correlation coefficient (PCC) and extreme learning machine (ELM) optimized by ant colony optimization (ACO) is proposed. Firstly, the vibration signal is decomposed by SGMD, and several symplectic geometry components (SGCs) are obtained, which can effectively capture the signal characteristics. Secondly, the constrains based on CDF, PCC and variable entropy weighted matrix (VEWM) are adopted to reconstruct SGCs into weighted symplectic combined components (WSCCs). Then calculate the power spectrum entropy (PSE) weighted singular values as the fault feature vectors. Finally, the ELM optimized by ACO is introduced to perform fault classification. Simulation and experimental results show that the proposed method can effectively extract rich fault features from vibration signals, and has a higher diagnosis accuracy, up to 99.18%. [ABSTRACT FROM AUTHOR]

Plate-like carbon fiber composite is an important component of the main bearing structure of spacecraft and high-speed trains, which has significant advantages in reducing the dead weight and energy consumption, and improving the carrying capacity. However, due to long-term exposure to complex service environments, material properties will be changed, these changes accumulated to a certain extent will lead to structural damage, threatening equipment operation and personal safety. To facilitate the attainment of mastering structural health states, this paper proposes a damage position imaging and tracking technology based on linear annular path damage probability distribution and ultrasonic guided wave for composite plate-like structures. Firstly, according to the dispersion curve, the propagation velocity characteristics of guided wave on composite structure are analyzed. Then, combined with the limited number of measured propagation velocities, the group velocity of the A0 mode in each direction is fitted. Based on the fitted velocity, the time-of-flight (TOF) map for all search points on each path can be pre-constructed. Subsequently, the damage TOFs and absolute TOF difference threshold are combined to obtain the annular damage probability distribution map of each sensing path. Ultimately, the total damage probability of the whole monitoring region is obtained by summing up the path damage probability, which can directly reflect the current damage position. Experimental results show that the proposed method has obvious advantages in imaging and tracking of single or multiple damage positions. It is hoped that the findings in this paper can provide new ideas for long-term damage state monitoring of the composite structures. • Each damage can be identified by a corresponding set of damage paths. • The DCR of the damage path can accurately contain the actual damage position. • Annular path damage probability method can effectively locate multiple damages. • Annular path damage probability method can effectively track multiple damages. [ABSTRACT FROM AUTHOR]

• Third-harmonic phase velocity matching nonlinear Lamb waves are used for the early fatigue damage detection of aluminum alloys. • The third harmonic first increases and then decreases with the accumulation of fatigue life. When the fatigue life reaches about 80%, the amplitude of the third harmonic declines sharply. • The third-order nonlinear parameters increase continuously with the accumulation of fatigue life. When the fatigue life reaches about 80–90%, the third-order nonlinear parameters increase gradually and shows a downward trend. • The third harmonic and third-order nonlinear parameters are used as indicators of fatigue accumulation to realize detection of early fatigue damage. In this paper, third-harmonic phase velocity matching nonlinear Lamb waves are used for the early fatigue damage detection of aluminum alloys. An analysis is conducted regarding the relationship of third harmonic and third-order nonlinear parameters with fatigue accumulation. According to the analytical results, the amplitude of the third harmonic increases when the fatigue life falls below about 80%. However, when the fatigue life exceeds 80%, the third harmonic starts to decrease, and the third-order nonlinear parameters increase progressively before showing a downward trend. It can be found out that micro-cracks emerge with the accumulation of fatigue, thus reducing the amplitude of the third harmonic and causing the third-order nonlinear parameters to increase gradually and then decline. Furthermore, it is proposed to take the third harmonic amplitude and the third order nonlinear parameters as the indicators of early fatigue damage. [ABSTRACT FROM AUTHOR]

The identification of the damage state of Carbon fiber-reinforced plastic (CFRP) structure is the necessary information for ensuring the safety of CFRP structure. In this paper, the structural damage identification system using fiber Bragg grating (FBG) sensors and the damage identification method were investigated. FBG sensors were used to detect the structural dynamic response signal, which was generated by an active actuation way. Fourier transform and principal component analysis (PCA) were used to extract the damage characteristic. After that, the structural damage identification model was constructed based on extreme learning machine (ELM), whose input is the damage characteristic and output is the damage state. Finally, the damage identification system was established and verified on a CFRP plate with 160 mm $$\,\times$$ 160 mm experiment area. The experimental results showed that the identification accuracy was more than 90 %. This paper provided a reliable method for CFRP structural damage identification. [ABSTRACT FROM AUTHOR]

Lamb wave is widely studied in the non-destructive testing of composite materials due to its wide detection range and high sensitivity to defects. In order to solve the problem of extracting the effective signal of damage in a strong noise environment to improve the accuracy of damage location determination. A damage location imaging method based on power spectral density and Lamb wave tomography is proposed in this paper. The damage location imaging of composite materials under strong noise environment is realized through simulation and experiment respectively. Firstly, the simulation analysis the propagation characteristics of Lamb waves in composite materials. The circular sensor array is evenly arranged on the composite material. Each sensor is used as an actuator to generate Lamb wave in turn clockwise. Other sensors are responsible for collecting signals. Strong noise is added to the collected signal to simulate the signal collected in the strong noise environment. Finally, the damage information is characterized by power spectral density to determine the damage factor, and by using the probability imaging algorithm, the damage location imaging is totally realized. The experimental results show that the maximum error of imaging positioning for single damage and multiple damage under strong noise environment is 5.10 mm and 8.04 mm, respectively. This method does not need to pretreatment the signal with strong noise, and can directly image the original signal. At the same time, the extraction process of complex reflected signals is avoided, and it has a great potential in the location and identification of composite materials damage in a strong noise environment. [ABSTRACT FROM AUTHOR]

• The third harmonic phase velocity matching is used to achieve early fatigue damage detection. • The relationship between the third harmonic signal and the length of fatigue cracks is shown. • The third harmonic approximate phase velocity matching also enables early fatigue damage detection. The phase velocity matching Lamb waves are considered as an effective solution to excitation in the detection of nonlinear ultrasound early fatigue damage. In general, it is difficult to meet the phase velocity matching in real-world measurement. In this paper an approach to approximate phase velocity matching early fatigue damage detection is proposed. With the third harmonic approximate phase velocity matching modes treated as excitation frequencies by the Lamb wave dispersion curve, an analysis is conducted with regard to the time–frequency diagrams of the response signals at different frequencies. The change in the third harmonic amplitude with crack growth is analyzed by Fast Fourier Transform (FFT), which leads to the results suggesting that the third harmonic amplitude declines with crack growth. Moreover, a comparison is performed in the normalized relative third-order nonlinear parameters (NRTNP) of phase velocity matching and approximate phase velocity matching, with the relative deviation falling within 0.12. [ABSTRACT FROM AUTHOR]

This work offers a defect segmentation approach for the nondestructive testing of tunnel lining internal defects using Ground Penetrating Radar (GPR) data. Given GPR synthetic data, it maps the internal defect structure, using a CNN named Segnet coupled with the Lovász softmax loss function, which enhances the accuracy, automation, and efficiency of defect identification. Experiments with both synthetic and actual data show that our innovative method overcomes problems in standard GPR data interpretation. A physical test model with a known defect was developed and manufactured, and GPR data was acquired and analyzed to verify the approach. • Defect segmentation is our proposed tunnel lining defect detection. • Inputting GPR directly into CNNs profiles internal lining defects. • CNN correctly identified defect types and location, and achieved reliable results. • Segnet was introduced to the defect segmentation method for more accurate results. • Model building and data processing verified the proposed method. [ABSTRACT FROM AUTHOR]

• Earlier bolt loosening warning during the stage of thread looseness is achieved. • The dependence of the nonlinear modulation and the bolt loosening is non-monotonic. • Both nonlinear elastic and amplitude dissipation effect contribute to the sideband. • Linear swept signal is applied for probing wave in VAM technique. • The experiment for the the sand spreading device of high-speed trains is carried out. Structural health monitoring (SHM) has played a crucial role in continuously assuring adequate integrity across equipment, especially early loosening estimation of bolt joints. Recently, the vibro-acoustic modulation (VAM) technique for bolt early loosening monitoring has received much attention due to its high sensitivity to contact-type defects. However, the current early loosening monitoring mostly focuses on the bolt head and interconnecting component loosening while ignoring the earlier loosening stage. The central contribution of this work is to develop full life-cycle monitoring for bolt loosening and consequently achieve even earlier warning in the stage of thread loosening. Firstly, regarding the bolt joints in the sand spreading device of high-speed trains, a modified VAM method, in which the designed sensor layout is more applicable for engineering and a swept signal is applied as a probing wave to overcome the dependence of detection accuracy on the exact resonance frequency excitation, is conducted for the full life-cycle monitoring. Secondly, the mechanisms, performances and dependency on the applied torques of the loosening-induced nonlinearity are explored with the proposed nonlinear indexes, theoretically and experimentally. The results demonstrate that both the nonlinear elastic mechanism and the amplitude-dependent dissipation effect contribute to such nonlinearity. Further analysis suggests two valuable and not yet mentioned findings: 1) there is a thread loosening stage that exhibits sufficiently sensitive nonlinear modulation index variations to predict bolt early looseness before the bolt head and interconnecting component loosening; 2) such nonlinear indexes are non–monotonically related to the torque levels in both the above two loosening stages, consistent with theory. Further, such full life-cycle monitoring is achieved by associating such nonlinear indexes and a linear transmitted energy index. All of the obtained results will aid in understanding the nonlinear symptoms under various bolt tightening levels and further improving the reliability of the VAM-based bolt early loosening monitoring. [ABSTRACT FROM AUTHOR]

The travel time computation of microseismic waves in different directions (particularly, the diagonal direction) in three-dimensional space has been found to be inaccurate, which seriously affects the localization accuracy of three-dimensional microseismic sources. In order to solve this problem, this research study developed a method of calculating the P-wave travel time based on a 3D high-order fast marching method (3D_H_FMM). This study focused on designing a high-order finite-difference operator in order to realize the accurate calculation of the P-wave travel time in three-dimensional space. The method was validated using homogeneous velocity models and inhomogeneous layered media velocity models of different scales. The results showed that the overall mean absolute error (MAE) of the two homogenous models using 3D_H_FMM had been reduced by 88.335%, and 90.593% compared with the traditional 3D_FMM. On that basis, the three-dimensional localization of microseismic sources was carried out using a particle swarm optimization algorithm. The developed 3D_H_FMM was used to calculate the travel time, then to conduct the localization of the microseismic source in inhomogeneous models. The mean error of the localization results of the different positions in the three-dimensional space was determined to be 1.901 m, and the localization accuracy was found to be superior to that of the traditional 3D_FMM method (mean absolute localization error: 3.447 m) with the small-scaled inhomogeneous model. [ABSTRACT FROM AUTHOR]

The phase-to-height imaging model, as a three-dimensional (3D) measurement technology, has been commonly applied in fringe projection to assist surface profile measurement, where the efficient and accurate calculation of phase plays a critical role in precise imaging. To deal with multiple extra coded patterns and 2 π jump error caused to the existing absolute phase demodulation methods, a novel method of phase demodulation is proposed based on dual variable-frequency (VF) coded patterns. In this paper, the frequency of coded fringe is defined as the number of coded fringes within a single sinusoidal fringe period. First, the effective wrapped phase (EWP) as calculated using the four-step phase shifting method was split into the wrapped phase region with complete period and the wrapped phase region without complete period. Second, the fringe orders in wrapped phase region with complete period were decoded according to the frequency of the VF coded fringes and the continuous characteristic of the fringe order. Notably, the sampling frequency of fast Fourier transform (FFT) was determined by the length of the decoding interval and can be adjusted automatically with the variation in height of the object. Third, the fringe orders in wrapped phase region without complete period were decoded depending on the consistency of fringe orders in the connected region of wrapped phase. Last, phase demodulation was performed. The experimental results were obtained to confirm the effectiveness of the proposed method in the phase demodulation of both discontinuous objects and highly abrupt objects. [ABSTRACT FROM AUTHOR]

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study's proposed method and its potential in processing complicated microseismic datasets. [ABSTRACT FROM AUTHOR]

• 1. An in-situ variable differential reflectance spectroscopy (DRS) model is proposed; • 2. A temperature error compensation method for chemical vapor deposition (CVD) growth is introduced; • 3. The CVD growth of molybdenum disulfide was monitored in situ by the method mentioned above. Here we introduce the spectral measurement model of online reflectance spectra aimed at the variable temperature environment. The model combines the traditional differential reflectance spectroscopy (DRS) model with the temperature error compensation method. The blank experiments and molybdenum disulfide (MoS 2) growth experiment prepared by chemical vapor deposition (CVD) were monitored in situ by using the new DRS model proposed in this work. The characteristic peaks of MoS 2 at 1.82 eV and 1.99 eV were successfully discovered in the spectra processed by the temperature error compensation method. This observation reveals that MoS 2 thin films have grown. The morphology and Raman properties of MoS 2 domains were off-line characterized using optical microscopy and Raman spectroscopy. The experimental results indicate that the spectral model and the temperature error compensation method can contribute to the real-time growth characterization of two-dimensional material (2D). It is of great significance for the preparation and application of 2D materials. [ABSTRACT FROM AUTHOR]

This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector's prior-knowledge, which can lead to advantages in automatic ultrasonic testing. [ABSTRACT FROM AUTHOR]

Here, we synthesized monolayer MoS 2 under atmospheric pressure by chemical vapor deposition (CVD) using a quartz tube with a large-diameter up to 100 mm. The morphology of MoS 2 thin films along the airflow direction was initially observed by the optical microscope. Raman spectra and atomic force microscopy (AFM) were utilized to characterize the thickness of samples. Moreover, the optical properties of four regions with continuous different coverage were systematically investigated by differential reflectance spectroscopy (DRS). It is found that MoS 2 thin films in the sub-monolayer structure have exhibited the same energy positions of optical characteristic peaks, including A exciton peak at 1.91 eV, B exciton peak at 2.08 eV and C exciton peak at 2.84 eV. And the DRS shows that the intensity of B exciton peak rises at the rate of ~0.46 monolay−1 (ML−1). These results provide an insight for monitoring the growth and evolutions of MoS 2 thin films with different coverage. Here, two significant outcomes of the research are: • The synthesis of MoS 2 thin films by CVD under atmospheric pressure using a 100 mm-diameter quartz tube, which is less commonly reported but is necessary. • The systematic analysis of DRS with continuous different coverage. The results highlight the intensity of characteristic peaks has a correlation with the film coverage. [ABSTRACT FROM AUTHOR]

• Based on the synchrosqueezing wavelet transform, the strong noise of Lamb wave can be filtered out. • Based on SAE algorithm, damage location and quantitative identification are realized simultaneously. • Incorporates synchrosqueezing wavelet transform and SAE algorithm. • Five other intelligent methods are explored as comparisons. This study proposes a technique for damage location and quantitative identification for composites under strong noise background on the basis of synchro squeezing wavelet transform and stack autoencoder algorithm. Firstly, the simulation of the mechanism of Lamb wave and damage of different degrees were conduct in this stage. Secondly, the simulation of the actual damage was carried out in the selecting composites with strain field of the structure changed by different masses (within the range of 3 grades) and mass blocks at different locations (64 regions). Moreover, the white noise (3 dB) was introduced to the signal detected by the sensor in order to simulate the Lamb Wave signals collected from the sensors under the background of strong noise. Actually, the training sample is composed of 26,880 (3 × 64 × 140) signals. In addition, the synchronized wavelet transform was proposed to denoise the signal through employing strong noise. After that, Fourier Transform was applied to extract damaged frequencies. Additionally, the input of the stack autoencoder was the frequencies, and that output was the result of corresponding damage, and in the meanwhile the damage identification model was established as well. Finally, the test data which had been processed was then input into the damage identification model for further testing and analysis. It was finally concluded that 191(total 192) samples were correctly identified, and the correct recognition rate arrived as high as 99.48%. It can identify different degrees of damage in different locations. It was demonstrated by the results that the method is highly accurate, with extensive application potential in instantaneous locating and quantitative recogniting damages in the composite plate under the condition of strong noise exists. [ABSTRACT FROM AUTHOR]

In order to deal with the problem of composite damage location, an imaging technique based on differential signal and Lamb wave tomography was proposed. Firstly, the feasibility of the technique put forward was verified by simulation. In this process, the composite model was regularly set down by the circular sensor array, with each sensor acting as an actuator in sequence to generate Lamb waves. Apart from that, other sensors were used to collect response signals. With regard to the damage factor, it was mainly determined by the difference between the damage signal and the non-damage signal. The probabilistic imaging algorithm was employed to carry out damage location imaging. Then, experiments were carried out so as to study the selected composite plate. Finally, the tentative outcomes have illustrated that the maximum error of damage imaging position was 7.07 mm. The relative error was 1.6%. In addition, the method has the characteristics of simple calculation and high imaging efficiency. Therefore, it has large technical potential and wide applications in the damage location and damage recognition for composite material. [ABSTRACT FROM AUTHOR]

• NIR spectroscopy was used for compositional analysis of cement raw meal. • The proposed method takes only 3–5 min for each measurement. • NIR spectra were correlated with oxides content by establishing siPLS models. • The established models were optimized by using different preprocessing methods. Near infrared (NIR) diffuse reflectance spectroscopy, combined with chemometrics techniques was proposed as a fast and accurate method applied for determination of main components (CaO, SiO 2 , Al 2 O 3 , Fe 2 O 3) in cement raw meal. The analyses were implemented by correlation of NIR spectra with reference method made by X-ray Fluorescence (XRF), using different regression methods such as partial least square (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) with multiple pretreatment methods. The optimal models obtained by using siPLS algorithm showed root-mean-square-error-of-prediction (RMSEP) from 0.0379 to 0.1715, the correlation coefficient (R p) from 0.7294 to 0.9304 and average prediction error from 0.03% to 0.13%. The proposed method is cheaper and safer, and it takes much less time (3–5 min) than the XRF method (40–60 min) for each measurement. Our results demonstrate that NIR diffuse reflectance spectroscopy technology can be applied to quantitative determination of the four oxides in cement raw meal. [ABSTRACT FROM AUTHOR]

Determining oxides content in cement raw meal with near infrared (NIR) spectroscopy, associated with partial least square (PLS) regression, is fast and potential for cement industry to realize cement raw material proportioning control. However, it has hardly been studied. Backward interval PLS (biPLS) with genetic algorithm (GA-biPLS) were applied to select characteristic variables closely related to the concentration of oxide of interest to establish calibration model. The optimal GA-biPLS models showed that the determination coefficient (R p 2) and root mean square error of prediction (RMSEP) were 0.8857 and 0.0994 for CaO, 0.8718 and 0.1044 for SiO 2 , 0.7417 and 0.0693 for Al 2 O 3 , 0.5404 and 0.0387 for Fe 2 O 3 , correspondingly. These results indicate that GA-biPLS can select less variables with better prediction performance by comparison with PLS and biPLS, the NIR spectroscopy combined with GA-biPLS algorithm is a fast, accurate and reliable alternative method for determination of oxides content in cement raw meal. Unlabelled Image • Near infrared spectroscopy was applied for component analysis of cement raw meal. • NIR analysis takes only 3 to 5 min per sample, much less than XRF. • Backward interval PLS with genetic algorithm was proposed for variable selection. • GA-biPLS models with less variables and better accuracy were established. [ABSTRACT FROM AUTHOR]

The in-situ observation is of great significance to the study of the growth mechanism and controllability of two-dimensional transition metal dichalcogenides (TMDCs). Here, the differential reflectance spectroscopy (DRS) was performed to monitor the growth of molybdenum disulfide (MoS2) on a SiO2/Si substrate prepared by chemical vapor deposition (CVD). A home-built in-situ DRS setup was applied to monitor the growth of MoS2 in-situ. The formation and evolution of monolayer MoS2 are revealed by differential reflectance (DR) spectra. The morphology, vibration mode, absorption characteristics and thickness of monolayer MoS2 have been confirmed by optical microscopy, Raman spectroscopy, ex-situ DR spectra, and atomic force microscopy (AFM) respectively. The results demonstrated that DRS was a powerful tool for in-situ observations and has great potential for growth mechanism and controllability of TMDCs prepared by CVD. To the best of the authors' knowledge, it was the first report in which the CVD growth of two-dimensional TMDCs has been investigated in-situ by reflectance spectroscopy. [ABSTRACT FROM AUTHOR]

In this paper, based on two additional phase-coding patterns, an improved phase demodulation method is proposed. First, six equally spaced coding phases in the interval [$ - \pi $-π, $\pi $π] are embedded in different periods of the coded fringes following a certain sequence. Subsequently, since a group of phase orders can be uniquely determined by the four adjacent coding phases, the phase-order map of the object can be generated. To ensure the accuracy of decoding results, the interference coding numbers should be corrected in advance. In the meantime, the connected regions exhibiting the same orders are classified and then labeled for simplifying the decoding process. The simulation results verify the feasibility of the proposed method. By two groups of 3D imaging experiments, the applicability of this method to multiple objects and discontinuous objects is confirmed.

We present an original method to improve the spatial resolution of a Brillouin distributed temperature and strain sensing system (DTSS). This method is shown to substantially improve the spatial resolution, while simultaneously strengthening the Brillouin backscattered light, which is based on the combination of an internal modulation of the laser source and an external modulator to generate two separate light pulses with different central wavelengths and pulse widths. Moreover, a novel Brillouin signal detection method, which we called isogenous heterodyne detection, is introduced, which is equivalent to a heterodyne detection scheme but is only with Rayleigh and Brillouin backscattered light without the need of an extra reference light. These new technical approaches have been incorporated into a fiber optic DTSS with 13 km single-mode fiber, which clearly successfully demonstrated all the advantages over conventional DTSS approaches in theory and the feasibility in experiment.