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A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault signals in wind turbine bearings, which are easily concealed by strong background noise and susceptible to intermittent interference. The adaptive local iterative filtering (ALIF), as a novel nonstationary signal processing technique, can perform adaptive filtering based on the signal itself characteristics. However, its mode mixing is an annoying problem. To relieve this problem, the noise-assisted CEALIF-based filtering is proposed. Nonetheless, the ambient noise present in the original signal is retained in the component of interest. Maximum correlation kurtosis deconvolution (MCKD) is an effective tool for enhancing periodic pulses. However, its deconvolution parameters need to be set manually, and are more demanding when the bearing failure is weak. To address this circumstance, this paper introduces the particle swarm algorithm (PSO) to solve the optimal deconvolution parameters and proposes EMCKD. Firstly, by employing CEALIF, the original signal is adaptively filtered into a sequence of IMFs. Then, the IMF that best characterizes the fault information is selected based on the weighted kurtosis index (WKI). Finally, the shock components of the selected IMF are enhanced to extract the periodic shock based on EMCKD. The proposed approach can accurately extract fault characteristics by analyzing the whole life cycle signals of bearings and fault signals of a 1.5 MW direct-drive wind turbine within strong background noise. Further, the proposed approach is implemented for the compound fault extraction of bearings, and the compound fault information of the inner race as well as the outer race of the bearings is successfully extracted.

Object detection plays an important role in computer vision. It has a variety of applications, including security detection, vehicle recognition, and service robots. With the continuous improvement of public databases and the development of deep learning, object detection has witnessed significant breakthroughs. However, the object detection of sweeping robots during operations should consider various factors, including the camera angle, indoor scenery, and identification of object category. To the best of our knowledge, no corresponding database on these conditions has been developed. In this study, we review the development of object detection based on deep learning in computer vision. Then, we propose a large-scale publicly available benchmark dataset called object detection for sweeping robots in home scenes (ODSR-IHS). The dataset has 6,000 images and 16,409 instances of 14 object categories. Finally, we evaluate several state-of-the-art methods on the ODSR-IHS dataset and transplant them to the hardware to establish a benchmark dataset for object recognition research on sweeping robots.

Due to constant change of rotational speed and low frequency property of vibration signal, it is difficult to identify the weak time-varying fault features of direct-driven wind turbines during early period. In view of the problems of variable speed and strong time-varying characteristics, the traditional Time-Frequency (TF) analysis methods always generate a low TF resolution. Rearrangement Method (RM), as a mathematically excellent and efficient solution, is used to improve the TF energy localization, whereas it cannot reconstruct signals. To make TF plane more readable, RM can be utilized to make up the deficiency of the frequency-reassigned Synchrosqueezing Transform (SST). On such basis, this paper puts forward a novel TF analysis method, namely RFSST2, which has drawn on the advantages of RM and Second-Order Synchrosqueezing Transform based on STFT (FSST2). This method is used to sharpen the blurry TF energy for strong time-varying modulated signals. Besides, it can reconstruct the perfect signals. After the extraction the rotation speed curve from high-resolution TF plane generated by RFSST2, the order analysis without tachometer is performed to address the problem of variably rotating speed. To prove the proposed method is superior in processing complex multi-component signals, we respectively estimate the variable speed of wind turbines and deal with the fault signals from the gear fault simulation test-bed and from actual 1.5 MW direct-drive wind turbines. The analysis result obtained by comparing different TF methods shows that the method put forward by this paper can effectively extract time-varying fault characteristics under non-stationary condition.

Mineralized collagen (MC) is a biomaterial that is commonly used in the treatment of bone defects. However, the inflammatory response after biomaterial implantation is a recurrent problem that requires urgent attention. Our previous studies on MC-macrophage interactions were descriptive but we did not perform an in-depth analysis on a genetic level to investigate the underlying mechanisms. In this study, we cultured RAW264.7 cells on MC or collagen and examined the proliferation of the macrophages by Cell Counting Kit-8 assay. We sequenced the RNA of the cultured cells to discover differential gene expression patterns and found that a total of 1183 genes were differentially expressed between the MC- and collagen-cultured groups, of which 396 genes were upregulated and 787 were downregulated. Gene ontology analysis revealed that biological processes in MC-cultured cells, such as inflammation and innate immunity, were downregulated; whereas nucleosome assembly, megakaryocyte differentiation, and chromatin assembly were upregulated. We identified several pathways associated with immunity that were significantly enriched using the Kyoto Encyclopedia of Genes and Genomes. Furthermore, we validated the differentially expressed genes from RNA sequencing by quantitative real-time polymerase chain reaction. This study provides insight into the macrophage phenotype based on the microenvironment, which is the foundation for the clinical application of MC-based interventions.

Swelling deformation tests of GaoMiaoZi (GMZ01) bentonite were performed at 25, 40 60, and 90 °C in distilled water. Experimental results suggested that the maximum swelling strain of GMZ01 bentonite increased with temperature, due to the osmotic swelling dominated the swelling. The em − σv relation for compacted bentonite at a same temperature can be expressed as e m = K σ v D - 3 (em is montmorillonite void ratio, σv is vertical stress). The fractal dimension, D, of GMZ01 bentonite was unaffected by the temperature according to the results of N2 adsorption tests. Calculated by the diffuse double layer (DDL) model under the condition of osmotic swelling, a linearly relation was found between the montmorillonite swelling coefficient K and temperature Tc. Combing the fractal em − σv relation and linear K − Tc relation, a simple method for evaluating the swelling of GMZ01 bentonite affected by temperature was proposed.

Repairing damage in the craniofacial skeleton is challenging. Craniofacial bones require intramembranous ossification to generate tissue-engineered bone grafts via angiogenesis and osteogenesis. Here, we designed a mineralized collagen delivery system for BMP-2 and vascular endothelial growth factor (VEGF) for implantation into animal models of mandibular defects. BMP-2/VEGF were mixed with mineralized collagen which was implanted into the rabbit mandibular. Animals were divided into (i) controls with no growth factors; (ii) BMP-2 alone; or (iii) BMP-2 and VEGF combined. CT and hisomputed tomography and histological staining were performed to assess bone repair. New bone formation was higher in BMP-2 and BMP-2-VEGF groups in which angiogenesis and osteogenesis were enhanced. This highlights the use of mineralized collagen with BMP-2/VEGF as an effective alternative for bone regeneration.

This paper investigates distributed adaptive bipartite consensus for second-order multi-agent systems (MASs) with bounded disturbances under a signed graph. Based on adaptive variable structure control strategies, an adaptive bipartite consensus protocol is proposed, which guarantees that the bipartite consensus can be realized if the signed graph is structurally balanced and connected. Furthermore, the external disturbances are attenuated effectively by the variable structure control. And the obtained results are applied to adaptive consensus of disturbed second-order MASs under cooperative networks. Finally, a numerical simulation is presented to demonstrate the theoretical finding.

Tensor decomposition technique has been widely used in multichannel signals processing for its distinct superiority. The early fault feature signal of bearing is weak and easily inundated by ambient noise. In addition, the interference signals generated by other mechanical components also have a serious impact on the result of fault diagnosis. Aiming at the above issues, and built on the methods of generalized non-convex tensor robust principal component analysis (GNCTRPCA) and tensor singular value kurtosis (TSVK), this paper introduces a new fault diagnosis technique for multichannel bearing signals. First, the attractor tensor is formed by reconstructing the acquired multichannel signals in phase space. Second, based on tensor singular value decomposition (TSVD), the tensor robust principal component analysis (TRPCA) can provide a favorable noise reduction performance. However, TRPCA usually lowers the amplitude of useful singular value tubes (SVTs). To tackle this problem, the GNCTRPCA method is proposed to avoid the amplitude reduction. Third, a new TSVK method is introduced to determine the reconstructed order of SVTs, so as to extract the multichannel fault feature signals. Finally, the bearing fault type can be identified by comparing the peak frequencies of the extracted signals with the theoretical fault-related frequencies. Simulation analysis and experiment studies verify the principle and effectiveness of the proposed technique.

Diabetic retinopathy (DR) is the leading cause of decreased vision and blindness globally. The aim of this study was to understand the role of physcion 8-O-β-glucopyranoside (PG) in high glucose (HG)-induced DR and to investigate whether lncRNA NORAD/miR-125/STAT3 signalling was the underlying mechanism involved in DR. To this end, the serum levels of NORAD, miR-125, and STAT3 were determined in patients with DR. The APRE-19 cells were subjected to HG treatment to construct the cell model of DR. HG-disposed APRE-19 cell injury was assessed by detecting cell viability, apoptosis, concentrations of pro-inflammatory cytokines including TNF-α and IL-1β, and ROS generation. Moreover, the effect of PG on HG-disposed APRE-19 cell injury was investigated. NORAD was then overexpressed to investigate the combined effects of NORAD overexpression and PG on HG-disposed APRE-19 cell injury. Furthermore, the regulatory relationship between NORAD and miR-125 as well as miR-125 and STAT3 was investigated. The expression levels of NORAD and STAT3 were significantly increased in the serum of DR patients, while the miR-125 expression was decreased. The HG treatment-induced injury to APRE-19 cells, which were alleviated by PG treatment. Moreover, PG alleviated HG-disposed injury to ARPE-19 cells by decreasing NORAD. NORAD negatively regulated miR-125 expression and the combined effects of NORAD and PG on HG-disposed ARPE-19 cell injury were reversed by miR-125 overexpression. Furthermore, STAT3 was confirmed as a target gene of miR-125. Our results show that PG exerts protective roles in HG-disposed DR

In a high-level radioactive waste repository, bentonite may react with the alkaline solution produced by cement degradation. In this study, bentonite was mixed with alkaline solution in a closed system and reacted for 3–24 months. Furthermore, swelling tests were conducted on the alkaline-dissolved bentonite immersed in distilled water. The swelling deformation decreased significantly with increases in the concentration of NaOH solution and reaction time, and this was mainly due to montmorillonite dissolution. The fractal e–p relationship (e is the void ratio and p is the vertical pressure) with two calculation coefficients (the swelling coefficient and the fractal dimension) was employed to determine the swelling of alkaline-dissolved bentonite. The fractal dimension increased slightly with increasing reaction time or concentration of NaOH solution, as the dissolution traces caused by the alkaline solution favoured an increase in the irregularity and fractality of the bentonite surface. The swelling coefficient decreased linearly with decreasing montmorillonite content. In addition, the swelling coefficient and the fractal dimension were related exponentially to the reaction time in alkaline solution. A relationship between the swelling of alkaline-dissolved samples and the reaction time was proposed, which might be used to assess the swelling properties of bentonite barriers that would be affected by long-term dissolution of the alkaline solution in a closed repository.

olling bearings are crucial components in mechanical, civil and aerospace engineering. The practical working conditions of rolling bearings are complex and tough, hence fault diagnosis of rolling bearings under varying operating conditions is very challenging. This paper proposes a robust fault diagnosis approach of rolling bearings using multivariate intrinsic multiscale entropy analysis and neural network under varying operating conditions. The proposed approach deals with multivariate signal collected from multi-sensor acquisition system to capture much dynamical characteristic information. Multivariate intrinsic multiscale entropy analysis consists of adaptive projection intrinsically transformed multivariate empirical mode decomposition with adaptive noise (APIT-MEMD-AN) and improved multivariate multiscale sample entropy (IMMSE) with smoothed coarse graining process. Intrinsic mode functions (IMFs) obtained by APIT-MEMD-AN depict dynamical properties of multivariate signals. IMMSE of certain orders IMFs are adopted as input values of back propagation (BP) neural network to achieve fault classification of rolling bearings. APIT-MEMD-AN and IMMSE endow the proposed approach with the underlying adaptivity and robustness, making the proposed approach a fully data driven and robust method. Theoretical derivations, numerical simulations and experimental results verify the effectiveness and superiority of the proposed approach. The research work demonstrates the proposed approach is promising in fault diagnosis of rotary machinery under varying operating conditions.

Volatile organic compounds (VOCs) are hazardous to the environment and human health. Thus, tremendous effort has gone into developing effective/efficient techniques for VOCs separation from their aqueous mixtures. In this work, a simple and versatile strategy was proposed to fabricate a new material for hydrophobic volatile organic compounds (VOCs) separation, in which a self-assembling hydrogel consisting of TEMPO-oxidized cellulose nanofibers (TOCN) and cationic guar gum (CGG) was used. Specifically, TOCN and CGG were deposited onto a filter paper via a layer-by-layer procedure, resulting in a TOCN/CGG hydrogel-coated filtering material, in which no crosslinking agent was used. The hydrogel is spontaneously formed by electrostatic interactions and hydrogen bonding. The as-prepared filtering material exhibits oil-repellence and underwater oleophobicity resulting from the formation of hydration layer on hydrogel, thus demonstrating good VOCs separation performance. One-layer of TOCN/CGG hydrogel coating (dried mass of 0.032 g·m-2) was proved to realize the VOCs separation (separation efficiency of around 99% and water flux of as high as 905.14 L·m-2·h-1). Further increase of the hydrogel layers has negligible influence on the separation efficiency although it decreases the water flux. This filtering material also exhibits good stability during recycling. Consequently, this TOCN/CGG self-assembling hydrogel-coated filtering material has great potential in VOCs-contaminated wastewater treatment.

Based on the piezoresistive effect, the piezoresistive constitutive relation of a carbon fiber mat under orthogonal strain was deduced. Considering the Poisson effect, the piezoresistive responses and measurement of the sensitivity factor of a polymer-matrix carbon fiber mat under bidirectional strain were studied by a two-times uniaxial tension loading method in different directions, which was pasted in the center area of a cruciform aluminum substrate. The relations between the resistance change rate and the orthogonal strains were established, the reasonability of which was confirmed by comparison with the experimental results. The results show that the longitudinal piezoresistive sensitivity factor C11 is 21.55, and the lateral piezoresistive sensitivity factor C12 is 24.15. Using these factors, the resistance change rate of another polymer-matrix carbon mat was predicted, which was made by the same technique, and the error between the predicted and the experimental results was 1.3% in the longitudinal direction and 6.1% in the lateral direction.

The present study was aimed to develop a novel combination therapeutic strategy of gene therapy and immunotherapy for efficiently treatment of colorectal cancer (CRC). To achieve that goal, the polyethylene glycol-modified poly (2-(N,N-dimethylamino) ethyl methacrylate) (PEG-PDMAEMA)-based nanoparticles loaded with protein tyrosine phosphatase non-receptor type 6 (PTPN6) (NP-PTPN6) was developed first followed by conjugation with anti-PD-L1 monoclonal antibodies (aPD-L1) atezolizumab (aPD-L1NP-PTPN6). Importantly, the aPD-L1 was conjugated on the surface of NP-PTPN6 by the matrix metalloproteinases (MMPs)-cleavable linkage PLGLAG. Therefore, the aPD-L1 would be completely released once the aPD-L1NP-PTPN6 was entrapped into tumour tissues as demonstrated by the release assay. Tumour targeting assay demonstrated the aPD-L1NP-PTPN6 have high affinity to CRC cells and resulted in excellent tumour targeting drug delivery efficacy. Additionally, anti-tumour effect evaluation revealed that the aPD-L1NP-PTPN6 has greater ability to inhibit the growth, invasion and migration of CRC cells and finally led to longer survival time of tumour-bearing mice than other treatments. Further mechanisms studies demonstrated that treatment of CRC cells with aPD-L1NP-PTPN6 contributed to significant suppression of the MAPK/ERK signalling pathway. Besides, it was further demonstrated that treating CRC with aPD-L1NP-PTPN6 resulted in up-regulation of NK cells and T cells percentage within the tumour tissues.

Crack localization in running rotors is very important and full of challenges for machinery operation and maintenance. Characteristic deflection shapes or their derivatives based methods seem to be promising for crack localization in rotors. Despite the substantial advantages, several critical issues still need to be addressed to enhance the efficiency of this kind of method for practical applications. Two problems are considered in this work: 1. How to localize single or multiple cracks accurately avoiding the interference of commonly existing steps without baseline information on pristine rotors, 2. How to improve the crack localization performance under a noisy environment. To circumvent the issues, a novel baseline-free adaptive crack localization method is proposed based on data fusion of multiscale super-harmonic characteristic deflection shapes (SCDSs). In this method, crack induced asymmetry and nonlinearity of crack breathing are utilized to simultaneously eliminate the interference from the steps without a reference model. To enhance the noise robustness, the multiscale representations of SCDSs are made in Gaussian multiscale space, and Teager energy operator is applied to the multiscale SCDSs to amplify the crack induced singularities and construct the multiscale Teager super-harmonic characteristic deflection shapes (TSCDSs). Moreover, fractal dimension is designed as an evaluator to select the proper multiscale TSCDSs for data fusion adaptively. Then, a new damage index is derived for crack localization by Dempster-Shafer&rsquo, s (D-S) evidence fusion of the adaptively selected multiscale TSCDSs. Finally, the feasibility and the effectiveness are verified by both numerical and experimental investigations.

Sulfamethoxazole (SMX) is a commonly used antibiotic for both human and animals. The frequent detection of SMX in natural water bodies and sediment has become an issue of great environmental concern due to its potential risk to induce antibiotic resistance in pathogenic bacteria. In the present work, the catalytic wet peroxide oxidation (CWPO) was investigated to remove SMX with sludge-derived carbon (SC) as a cheap alternative catalyst. Different acids were used to modify SC. It was found that SC modified with sulphuric acid (SC-H2SO4) demonstrated the best catalytic activity. The removal efficiency of SMX and TOC was 97.7% and 65.7%, respectively, after 260 min, at pH 5 with a dosage of 220 mg/L H2O2. The effects of temperature, initial pH and H2O2 dosage were also investigated. The study demonstrated that the increase of temperature could significantly improve the degradation of SMX from 10.0% at 20°C to 94.7% at 60°C.

As a novel time-frequency analysis method, adaptive local iterative filtering (ALIF) can decompose the time series into several stable components which contain the main fault information. In addition, the amplitude of singular value obtained by singular value decomposition (SVD) can reflect the energy distribution. Naturally, there are certain differences in the energy produced by different faults such as the broken tooth, wearing and normal. Thus, a novel method of mechanical fault classification method based on adaptive local iterative filtering and singular value decomposition is proposed in this paper. Firstly, ALIF method decomposed the original vibration signal into a number of stable components to establish an initial feature vector matrix. Then, the singular values energy corresponding to the feature matrix is employed as a criterion to identify various faults. Compared with the conventional EMD method by simulation experiments, ALIF method has obvious superiority in solving modal aliasing, which is more conducive to the advanced analysis. In this paper, the proposed method is employed to extract the fault information of rolling bearing fault signals from Case Western Reserve University Bearing Data Center. To further verify the effectiveness of the method, the case study is conducted at Drivetrain Diagnostics Simulator. To further illustrate the effectiveness of the method, the results obtained by this method are compared with EMD and EEMD. The results indicated the proposed method performs better in the classification of different mechanical faulty modes.

TiO2 Monodisperse microspheres (MMS) assembled by nanoporous spindles were successfully synthesized a facile hydrothermal method using P25 as precursor. The anatase TiO2 MMS possesses high surface area of 88.7 m2 g−1 with uniform diameter of ∼350 nm. The photovoltaic performance of MMS based photoanode was investigated in the dye-sensitized solar cell. Due to high dye loading amount, good light harvesting and suppressed carried recombination, the device based on MMS exhibited an excellent short-circuit current density of 16.15 mAcm−2 and high open-circuit voltage of 740 mV, which leads to the optimal power conversion efficiency of 8.43% indicating a ∼40% enhancement than the commercial P25 based device (5.99%).

Upconversion luminescence characteristic about YNbO4 powder material doped with Ho3+ and Yb3+ ions is reported. Ho3+/Yb3+ co-doped YNbO4 powder material is prepared through solid-state reaction, absorption spectrum of prepared sample is measured and spectral characteristic is calculated through Judd–Ofelt (J-O) theory. Emission spectrum of prepared sample excited under 980 nm laser diode (LD) is obtained, and results indicate that there are three groups of upconversion emission peaks located at 540 nm and 548 nm, 642 nm and 663 nm, 758 nm, respectively, which correspond to level transitions from 5F4 to 5I8, from 5F5 to 5I8, from 5F5 to 5I7 of Ho3+ ions. Curve fitting of relationship between upconversion luminescence intensity and working current of exciting LD is done, and the fitting results prove that this up-conversion luminescence are all two-photon absorption procedure.

For laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, baseline correction is an essential part for the LIBS data preprocessing. As the widely existing cases, the phenomenon of baseline drift is generated by the fluctuation of laser energy, inhomogeneity of sample surfaces and the background noise, which has aroused the interest of many researchers. Most of the prevalent algorithms usually need to preset some key parameters, such as the suitable spline function and the fitting order, thus do not have adaptability. Based on the characteristics of LIBS, such as the sparsity of spectral peaks and the low-pass filtered feature of baseline, a novel baseline correction and spectral data denoising method is studied in this paper. The improved technology utilizes convex optimization scheme to form a non-parametric baseline correction model. Meanwhile, asymmetric punish function is conducted to enhance signal-noise ratio (SNR) of the LIBS signal and improve reconstruction precision. Furthermore, an efficient iterative algorithm is applied to the optimization process, so as to ensure the convergence of this algorithm. To validate the proposed method, the concentration analysis of Chromium (Cr),Manganese (Mn) and Nickel (Ni) contained in 23 certified high alloy steel samples is assessed by using quantitative models with Partial Least Squares (PLS) and Support Vector Machine (SVM). Because there is no prior knowledge of sample composition and mathematical hypothesis, compared with other methods, the method proposed in this paper has better accuracy in quantitative analysis, and fully reflects its adaptive ability.

Profiting from the good ability in time–frequency (TF) representation (TFR), synchroextracting transform (SET) has been extensively used for non-stationary signal processing. Vibration signals of bearing often contain strong noise, whose time-varying features are easily contaminated by noise information, affecting the TF readability of SET. Effectively identifying the time-varying features from noise are important for fault diagnosis. For this purpose, by introducing the frequency-rotating operator and frequency-shifting operator of spline-kernelled chirplet transform (SCT), this paper studies a new time–frequency analysis (TFA) method called synchro spline-kernelled chirplet extracting transform (SSCET). It is shown that the proposed method can effectively reveal the variations of time-varying features while retaining the energy concentration in noisy cases. Besides, the proposed method uses the studied binary TF image separation strategy to extract the time-varying features of multi-component signals. The comparative analysis results in simulations verified its effectiveness in energy concentration and anti-noise. The proposed method is finally successfully applied to the analysis of a bat echo signal as well as the fault diagnosis of a rolling bearing.

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

In this paper, a novel multi-channel mechanical failure signal classification method based on augmented quaternion singular spectrum analysis (AQSSA) is proposed. Quaternion is used to couple four channels signal, and the quaternion trajectory matrixes can be developed as augmented quaternion matrix by using the feature of the quaternion. The singular value sequence including characteristic information can be extracted by quaternion singular value decomposition (QSVD) of the augmented trajectory matrix using its covariance matrix. The method of traditional singular spectrum analysis (SSA) can only analyze the single channel signal, however, AQSSA can fully use the correlation of multi-channel and reduce the loss of the effective information. Additionally, the main singular values are defined by some methods such as difference spectrum aimed, which has the limitation that major singular values can’t be obtained under the high background noise. Thus, a concept of partial mean of singular value sequence is proposed, and it can be set as the standard of evaluating the trend of singular value sequence. In order to testify the performance of the proposed method, the numerical simulation signal and the fault vibration signal of bearing are simultaneously adopted to verify its effectiveness. The results indicate that the effectiveness of mechanical fault classification by the proposed method is superior to the traditional SSA method and the method of permutation entropy.

We proposed a facile method to prepare ZnO@g-C 3 N 4 core/shell type photocatalyst via a two-step calcination. Thin layer of g-C 3 N 4 was homogeneously coated on ZnO nanoparticles with well-defined interface, which reduces the charge transfer resistance and results in distinct PL quenching. Therefore, the as-prepared ZnO@g-C 3 N 4 demonstrates significant photocatalytic improvement under visible-light irradiation than ZnO (~35 times) and g-C 3 N 4 (~5 times) due to the effective charge separation and suppressed carrier recombination. Moreover the ZnO@g-C 3 N 4 photocatalyst shows high stability which retains ~90% of its initial activity after 5-cycle reuse.

Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.

An experiment of He-Ne laser with 632 nm wavelength dazzling a CCD array detector is performed and some abnormal crosstalk phenomena are observed. The traditional “bucket” model is established and used to simulate the process of saturation and diffusion of charges within the potential wells. Unfortunately the simulation results cannot explain the experimental phenomena very well. Then, a finite element simulation model of laser dazzling CCD detector based on the diffusion theory of electrons and holes in semiconductors is established and simulation calculation is performed. The simulation and experimental results show that the trend of the simulation curves is consistent with the experimental curves. Therefore, the simulation model can be used to further study the effects of laser dazzling CCD detector.

The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

Biomaterials regulate macrophages and promote regeneration function, which is a new hot pot in tissue engineering and regenerative medicine. The research based on macrophage materials biology has appeared happy future, but related research on regulating macrophages and promoting tissue regeneration is still in its infancy. The surface roughness of biomaterials is one of the important factors affecting macrophage behavior. Previous study also found that the surface roughness of many biomaterials regulating macrophage polarization, but not including mineralized collagen (MC). In this study, we designed and fabricated MC with different roughness and investigated the influence of MC with different roughness on macrophages. In the study, we found that on the rough surface of MC, macrophages exhibited M1 phenotype-amoeboid morphology and high-level secretory of inflammatory factor (tumor necrosis factor-α and interleukin-6), while smoother surface exhibited M2 phenotype. These data will be beneficial to understand the mechanism deeply and enrich biomaterials tissue regeneration theory, provide a new train of thought biomaterials inducing tissue regeneration and repair and guide the optimum design of new biomaterials, development and reasonable applications.

Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals.

Dynamic model updating based on finite element method (FEM) has been widely investigated for structural damage identification, especially for static structures. Despite the substantial advances in this method, the key issue still needs to be addressed to boost its efficiency in practical applications. This paper introduces the updating idea into crack identification for rotating rotors, which has been rarely addressed in the literature. To address the problem, a novel Kriging surrogate model-based FEM updating method is proposed for the breathing crack identification of rotors by using the super-harmonic nonlinear characteristics. In this method, the breathing crack induced nonlinear characteristics from two locations of the rotors are harnessed instead of the traditional linear damage features for more sensitive and accurate breathing crack identification. Moreover, a FEM of a two-disc rotor-bearing system with a response-dependent breathing crack is established, which is partly validated by experiments. In addition, the associated breathing crack induced nonlinear characteristics are investigated and used to construct the objective function of Kriging surrogate model. Finally, the feasibility and the effectiveness of the proposed method are verified by numerical experiments with Gaussian white noise contamination. Results demonstrate that the proposed method is effective, accurate, and robust for breathing crack identification in rotors and is promising for practical engineering applications.

The existing time-frequency analysis (TFA) methods mainly highlight the time-frequency ridges of the interested components by optimizing the time-frequency plane to facilitate the extraction of the relevant components. Generalized demodulation (GD), order tracking (OT), and other methods are generally used in conjunction with the TFA methods to realize the transition from a time-varying signal to a stationary signal, and finally identify the fault feature through a time-frequency plane. Generally, it is necessary to clarify the accuracy of the estimated components such as the rotational frequency or the fault characteristic frequency (FCF) during the operation of the GD or OT methods. Unfortunately, it is not only difficult to extract and locate rotational frequency or FCF, but also complicated in the whole estimation process. In this paper, a simple yet readable method is proposed to reveal the fault feature of time-varying signals. First, the method only needs to extract an arbitrary instantaneous frequency (IF). This is different from the GD method which needs to estimate and locate all phase functions. Then, it converts all variable frequency curves into corresponding lines parallel to the frequency axis based on the extracted IF to determine the proportional relationship between the components. Finally, to further improve the readability of the final results, we reduce the dimension of the transformed time-frequency representation to generate a two-dimensional (2D) energy-frequency map with high resolution and the same proportion. Subsequently, the performance is validated by simulated and experimental data.

Absorptivity of three kinds of surface morphology, i.e., V-type groove surface, sinusoidal surface, and random distribution, is investigated using a rigorous electromagnetic theory and a finite element method. Influences of surface contour parameters (span distance, intersection angle, and height) and light wave parameters (incident angle and wavelength) on absorptivity are numerically simulated and analyzed for the three kinds of surfaces, respectively. Absorbing spectra about three silicon wafers with different surface roughness are recorded, and the results are coincident with simulated results.

Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

A group of BaGd2ZnO5 microcrystalline powders doped with Sm[Formula: see text], Dy[Formula: see text], Nd[Formula: see text] and Er[Formula: see text]/Yb[Formula: see text] were prepared by high temperature solid phase method, respectively. X-ray diffraction (XRD) analysis confirmed that all the products were pure BaGd2ZnO5 crystals, belonging to orthorhombic system and Pbnm space group. The emission spectra of Sm[Formula: see text], Dy[Formula: see text], Nd[Formula: see text] single-doped and Er[Formula: see text]/Yb[Formula: see text] double-doped samples under specific wavelength excitation were measured. The change of luminescence intensity at strong emission peak with doping concentration of doped ions was studied, and concentration quenching effect was found. The CIE color coordinates of the prepared samples were calculated. The results show that all coordinates doped with Sm[Formula: see text] are located in the orange red region. All coordinates of Dy[Formula: see text] doping samples are located in the yellow light region, but close to the white light region. All coordinates of Nd[Formula: see text] doping sample are located in the blue-green region. The upconversion luminescence of Er[Formula: see text]/Yb[Formula: see text] co-doped sample belongs to yellow light region.

In the framework of maximum-likelihood detection for image watermarking schemes, the conventional Generalized Gaussian Distribution (GGD), Cauchy and Student's t distributions often fail to model the pulse-like distributions, such as Discrete Cosine Transform (DCT) coefficient distribution. Meanwhile DCT DC coefficients are often neglected in the image watermarking schemes. In this paper an improved full band image watermarking algorithm with utilization of Weibull distribution modeling the DCT AC and DC coefficients is proposed. Experiments indicate that compared with other popluar distributions such as the GGD, the Weibull model gives a closer fit on the distribution of AC coefficients in absolute domain with a smaller Kullback-Leibler (KL) divergence and lower Mean Square Error (MSE). The watermarking scheme with Weibull modeling the DCT AC coefficients (Weibull-AC) exhibits strong robustness under the attack of scaling and median filtering. The watermarking scheme with Weibull modeling the DCT DC coefficients (Weibull-DC) yields a better detection accuracy for bright and more detailed images. Combining the above two advantages, the proposed Weibull based full band watermarking in DCT domain (Weibull-FB) further improves its robustness under the attack of JPEG compression and achieves 10.47 % overall increment in the detection accuracy compared with the baseline system while maintaining good invisibility in the view of structural similarity (SSIM).

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the main research focuses on the accurate estimation of instantaneous frequency (IF). However, the performance of TF analysis is greatly affected by the strong frequency modulation (FM) signal. In particular, the actual measured mechanical vibration signals always contain strong background noise, which decreases the resolution of TF representation, resulting in an inaccurate ridge extraction. To solve this problem, an improved penalty function based on the convex optimization scheme is firstly introduced for signal denoising. Based on the superiority of the linear chirplet transform (LCT) in dealing with modulated signals, the synchroextracting chirplet transform (SECT) is employed to sharpen the TF representation after the convex optimization denoising operation. To verify the effectiveness of the proposed method, the numerical simulation signals and the measured fault signals of rolling bearing are carried out, respectively. The results demonstrate that the proposed method leads to a better solution in rolling bearing fault feature extraction.

Magnetic rotor-bearing system has drawn great attention because of its several advantages compared to existent rotor-bearing system, and explicit Runge–Kutta method has achieved good results in solving dynamic equation. However, research on flexible rotor of magnetic bearing is relatively less. Moreover, explicit Runge–Kutta needs a smaller integral step to ensure the stability of the calculation. In this article, we propose a nonlinear dynamic analysis of flexible rotor of active magnetic bearing established by using the finite element method. The precise Runge–Kutta hybrid integration method and the largest Lyapunov exponent are used to analyze the chaos of the rotor system at the first- and second-order critical speed of the rotor. Experiment on chaos analysis has shown that compared with the explicit Runge–Kutta method, the precise Runge–Kutta hybrid integration method can improve the convergence step of calculation significantly while avoiding iterative solution and maintain high accuracy which is four times the increase of the integral step.

Aiming at the positioning and navigation system of indoor mobile robot, a fuzzy control method of indoor location navigation based on UWB communication technology is proposed. Firstly, based on the UWB radio signal, the mobile robot localization information is obtained by the twice triangular centroid localization algorithm, and the reactive fuzzy controller is used to realize the local path planning. An improved contour following method is proposed to make the mobile robot get out of the trap area quickly and reach the target. This method can effectively drive the mobile robot to reach the target in a complex and unknown environment with reasonable path. Finally, a fourwheel indoor mobile robot is taken as an example in the indoor environment of 10m × 10m. The experiment proves that the method can effectively drive the mobile robot to reach the target point in the unknown environment with the better path.

In this paper, a novel percussion-based bolt looseness monitoring approach using intrinsic multiscale entropy analysis and back propagation (BP) neural network is proposed. The percussion-caused audio signals of bolt connection are decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain intrinsic mode functions (IMFs). The IMFs are in order of the high-to-low instantaneous frequencies and contain underlying dynamical characteristic information of audio signals. The multiscale sample entropy (MSE) is improved by smoothed coarse graining process, and the proposed improved multiscale sample entropy (IMSE) values of certain IMFs can be adopted as indicators of different bolt looseness conditions. The intrinsic multiscale entropy analysis consisting of CEEDMAN and IMSE can properly extract underlying dynamical characteristic information during audio signal processing in bolt looseness condition identification. The obtained indicators, which are IMSE values at smallest scale factors, were employed as input in BP neural network for training and testing, to achieve accurate and stable bolt looseness condition monitoring. The effectiveness and superiority of the proposed approach has been validated by theoretical derivation and practical experimental researches, and the adaptivity and robustness of the proposed approach are also illustrated. The results of the researches demonstrate the proposed approach is promising in practical applications of bolt looseness monitoring.

To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional methods, a convex optimization framework is introduced by applying a parameterized non-convex penalty function to obtain the optimal rank truncation number. This method is inspirited by the performance that it is more perfectible than other rank truncation methods in inhibiting noise disturbance. A hierarchical and multiresolution application similar to the process of wavelet packet decomposition in modes selection is presented so as to improve the algorithm’s performance. With the modes selection strategy, the frequency spectrum of the reconstruction signal is more readable and interference-free. The improved DMD algorithm successfully extracts the fault characteristics of rolling bearing fault signals when it is utilized for mechanical signal feature extraction. Results demonstrated that the proposed method has good application prospects in denoising and fault feature extraction for mechanical signals.

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.

Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.

To improve the diagnostic accuracy of follicle ultrasound images detection, this paper proposed a method of cattle follicle ultrasound images detection based on HOG + Improved LBP + SVM. It calculated the Histogram of Oriented Gradient (HOG) feature for all cell in detection window, used improved Local Binary Pattern method to get gray feature, combined with the Support Vector Machine (SVM), it did the feature training and test experiment, last, the proposed method was compared with that single HOG feature detection single traditional LBP feature detection and HOG + traditional LBP feature detection. Experimental results showed, the proposed method can effectively describe and detect cattle follicle ultrasound images, and it has higher recognition accuracy.

The vibration signals of bearings and gears measured from rotating machinery usually have nonlinear, nonstationary characteristics. The local projection algorithm cannot only reduce the noise of the nonlinear system, but can also preserve the nonlinear deterministic structure of the signal. The influence of centroid selection on the performance of noise reduction methods is analyzed, and the multiscale local projection method of centroid was proposed in this paper. This method considers both the geometrical shape and statistical error of the signal in high dimensional phase space, which can effectively eliminate the noise and preserve the complete geometric structure of the attractors. The diagonal slice spectrum can identify the frequency components of quadratic phase coupling and enlarge the coupled frequency component in the nonlinear signal. Therefore, the proposed method based on the above two algorithms can achieve more accurate results of fault diagnosis of gears and rolling bearings. The simulated signal is used to verify its effectiveness in a numerical simulation. Then, the proposed method is conducted for fault diagnosis of gears and rolling bearings in application researches. The fault characteristics of faulty bearings and gears can be extracted successfully in the researches. The experimental results indicate the effectiveness of the novel proposed method.

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.