Band Division Research Articles

Weak Fault Feature Extraction in Non-Gaussian Noise Interference Based on Adaptive Recombination Empirical Wavelet Transform Incorporated by Sparse Coding Shrinkage

Abstract The fast kurtogram (FK) is a very useful tool in the field of fault diagnosis, but it also contains two drawbacks. On one hand, its kurtosis indicator is susceptible to random impulse interference, leading to frequency band mis-selection. On the other hand, the frequency band division method determined by the tree filter bank may cause under or over decomposition problems. Therefore, in this paper a novel fault diagnosis theory is constructed combining the adaptive recombination empirical wavelet transform (AREWT) with the envelope spectral energy ratio (ESER). Adaptive decomposition of frequency bands is first performed using AREWT. Afterwards, the ESER is proposed as a statistical indicator to choose optimal demodulation frequency band which overcomes the effects of non-Gaussian noise interference and improves diagnostic accuracy. To further highlight the fault elements of the selected components, the adaptive sparse coding shrinkage algorithm (ASCS) is introduced to sparsely denoising sensitive components. Correspondingly, clear fault feature frequency components could be extracted from envelope spectrum. Finally, the practicability and superiority of proposed AREWT-ESER approach were fully validated through numerical simulation signal and case studies.

Adaptive Ramanujan package decomposition method: a novel precise health indicator of rolling bearings

ABSTRACT In this paper, an adaptive Ramanujan packet decomposition (ARPD) method based on precise envelope segmentation is proposed to improve the accuracy of weak fault feature extraction and realise early non-destructive testing and condition evaluation of rolling bearings. Firstly, the power spectral density of the original signal is envelope-processed by accurate envelope segmentation method, so as to reduce the extreme points produced by interference frequency, and then realise the optimal frequency band division and reduce the influence of noise components. Secondly, the Ramanujan spectrum signal-to-noise ratio (RSSNR) index is introduced to quantify the fault characteristic information of the corresponding component in each frequency band. The largest component of the index is selected as the optimal mode, and the current number of layers is defined as the optimal number of modes. Finally, through the idea of iterative decomposition and RSSNR index, the problem that the optimal mode number is difficult to determine accurately is eliminated, and the optimal mode number is determined adaptively. The results of simulation and experimental signal analysis show that ARPD method can accurately divide the frequency bands and has excellent weak fault feature extraction ability, so it is an effective early non-destructive testing and condition evaluation method for rolling bearings.

Enhanced multi-layer Ramanujan decomposition: an effective rolling bearing condition monitoring method

ABSTRACT Due to the influence of strong noise, it is difficult to extract and identify the fault features of rolling bearing when it is slightly damaged, which greatly increases the risk of fault miss detection. In this paper, the enhanced multi-layer Ramanujan decomposition (EMRD) method is proposed. On the one hand, EMRD method adopts accurate spectrum segmentation technology (ASST) to realise adaptive frequency band division. ASST firstly minimises the frequency erosion caused by noise through prior-unknown blind deconvolution method based on SOSO (SOSO-PUBD), then obtains relatively pure frequency distribution through power spectral density function, and finally realises frequency band segmentation by minimum segmentation method. On the other hand, EMRD method obtains the optimal number of decomposition modes in the form of multi-layer decomposition, and selects the optimal mode by Ramanujan spectrum signal-to-noise ratio index. The comparative experimental results show that EMRD method has excellent accuracy of frequency band segmentation and meets the requirements of rolling bearing fault diagnosis.

Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals

Generalized Anxiety Disorder (GAD) is a chronic anxiety condition characterized by persistent excessive worry, anxiety, and fear. Current diagnostic practices primarily rely on clinicians’ subjective assessments and experience, highlighting a need for more objective and reliable methods. This study collected 10-minute resting-state electroencephalogram (EEG) from 45 GAD patients and 36 healthy controls (HC), focusing on six frontal EEG channels for preprocessing, data segmentation, and frequency band division. Innovatively, this study introduced the “Differential Channel” method, which enhances classification performance by enhancing the information related to anxiety from the data, thereby highlighting signal differences. Utilizing the preprocessed EEG signals, undirected functional connectivity features (Phase Lag Index, Pearson Correlation Coefficient, and Mutual Information) and directed functional connectivity features (Partial Directed Coherence) were extracted. Multiple machine learning models were applied to distinguish between GAD patients and HC. The results show that the Deep Forest classifier achieves excellent performance with a 12-second time window of DiffFeature. In particular, the classification of GAD and HC was successfully obtained by combining OriFeature and DiffFeature on Mutual Information with a maximum accuracy of 98.08%. Furthermore, it was observed that undirected functional connectivity features significantly outperformed directed functional connectivity when fewer frontal channels were used. Overall, the methodologies developed in this study offer accurate and practical identification strategies for the early screening and clinical diagnosis of GAD, offering the necessary theoretical and technical support for further enhancing the portability of EEG devices.

Tremor Estimation and Removal in Robot-Assisted Surgery Using Improved Enhanced Band-Limited Multiple Fourier Linear Combiner.

During a robot-assisted minimally invasive surgery, hand tremors in a surgeon's manipulation of the master manipulator can cause vibrations of the slave surgical instruments. This letter addresses this problem by proposing an improved Enhanced Band-Limited Multiple Linear Fourier Combiner (E-BMFLC) algorithm for filtering the physiological tremor signals of a surgeon's hand. The proposed method uses the amplitude of the input signal to adapt the learning rate and a dense division of the combiner bands for the higher amplitude bands of the tremor signals. By using the proposed improved E-BMFLC algorithm, the compensation accuracy can be improved by 4.5%-8.9%, as well as a spatial position error of less than 1mm. The results show that among all filtering methods, the improved E-BMFLC filtering method has the highest number of successful experiments and the lowest experimental time.

Similitude laws for dynamic response of beam-plate coupled structure

The beam-plate coupled structure is a typical structure in spacecraft solar wings, and the prediction of its mechanical environment is crucial for overall design, structure and mechanism subsystem design. However, it is usually difficult to perform ground simulation tests on spacecraft solar wings with full-size structure. Therefore, a scaled model is established based on similarity theory to forecast the dynamic response of the full-size structure. In this article, finite element (FE) and hybrid finite element-statistical energy analysis (FE-SEA) models of the beam-plate coupled structure are established according to the band division principle. Finite element model allows for detailed analysis of structural behaviour, while hybrid models incorporate statistical energy analysis to account for high-frequency dynamics. By integrating these approaches, a comprehensive understanding of the coupled structure’s response can be achieved, facilitating efficient design and optimisation processes. The scaling laws of the dynamic response are obtained by combining the dimensional analysis and equation analysis methods, respectively. The scaled models are designed for verification, and the natural characteristics and vibration responses predicted by the scaled models are basically consistent with those of the prototype. The findings indicate that the prediction accuracy derived from the FE-SEA model is higher, which provides a novel approach for the dynamic similarity of complicated structures such as the solar wings of spacecraft.

Cyclostationary harmonic product spectrum with its application for rolling bearing fault resonance frequency band adaptive location

Harmonic product spectrum (HPS) is an important tool in bearing fault diagnosis because it can quickly locate the fault resonance frequency band. However, its frequency band division strategy easily leads to over- or under-decomposition. This might cause the decomposed signal to contain too little fault information or too much interference. Although an adaptive HPS based on scale space representation for frequency band division is proposed, its frequency band division is driven by the data characteristics of the signal itself, rather than the fault characteristics in signal. It is difficult to completely separate faults from signals by frequency band division without considering fault characteristics. Therefore, an adaptive frequency band segmentation strategy aiming at fault characteristic, i.e., cyclostationarity, maximization is designed and introduced to improve HPS, thus proposing a new optimal fault resonance band location technique. It can achieve fault resonance band location accurately and resist the influence of various noise interferences.

Structural flexibility identification from impact test data through a subband estimation method

SummaryFlexibility is an important parameter reflecting bridge load‐carrying capacity. Dynamic testing is a fast and effective method to obtain the structural modal flexibility of small‐ and medium‐span bridges. The Deterministic‐stochastic subspace identification (DSI) algorithm is a well‐established structure identification method in the time domain. However, the estimation of damping, especially the modal scaling factor, is not always reliable due to inevitable measurement noise, which directly affects the identification accuracy of flexibility. This paper proposes a maximum likelihood estimation method in subbands (SMLE), which can be regarded as an add‐on method of the DSI algorithm because the initial parameters are obtained from the DSI algorithm. The processing of frequency band division is implemented first, and the frequency response function curve in each subband of the whole frequency range is fit separately. Then, subband cyclic iteration is proposed to improve the identification accuracy in a closely spaced mode system. The proposed SMLE method maintains the advantages of the DSI algorithm while improving the accuracy of parameter estimation and flexibility identification. Two lumped mass models are used to verify that the proposed method can effectively improve estimates to obtain a precise flexibility matrix and predict the displacement of the structure under static loading. Experimental example of a continuous girder bridge is considered to verify the availability and effectiveness of the proposed method in practice.

An adaptive reweighted-Kurtogram for bearing fault diagnosis under strong external impulse noise

In vibration-based fault diagnosis of rotating machinery, strong impulse noise constitutes a widely encountered source of external interference, thereby posing significant challenges to the effective extraction of diagnostic information. In this context, an adaptive reweighted-Kurtogram (ARKurtogram) method is proposed. The bearing transient fault signatures are accurately extracted through two aspects – that is, precise frequency band division and robust band selection indicator. Specifically, we use the dual-tree complex wavelet packet transform (DTCWPT) as the band division tool and propose the sub-bands rearranged and ensemble DTCWPT which addresses the band disorder problem, yields far richer band division patterns and maintains excellent performance of DTCWPT in signal decomposition. Meanwhile, we introduce a robust indicator characterizing periodic fault impulses to accurately select the fault band. In comparison with the classical kurtosis and the advanced reweighted-kurtosis, this indicator is insensitive to strong external impulse noise and its calculation process contains a sliding window averaging approach which makes the calculation entirely automated without human intervention. The validity of the ARKurtogram is investigated with the analysis of vibration signals measured from fault-injection experiments. The comparisons with the classical fast Kurtogram method and the advanced Autogram method highlight the advantages of the method in extracting bearing transient fault signatures from complex signals with strong external impulse noise. It shows high potential in the application of industrial bearing fault diagnosis.

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Empirical wavelet transform is often used to process global navigation satellite system (GNSS) bridge deformation monitoring data, but it leads to inaccurate band division and too many spurious modes. To address these problems, this study proposes an improved algorithm to identify bridge modal parameters, which combines improved empirical wavelet transform and robust independent component analysis (ICA). The proposed method adopts the autoregressive power spectrum of an improved covariance algorithm, instead of the Fourier spectrum, for band division. Additionally, it performs noise reduction and reconstruction of multi-channel GNSS monitoring data. The reconstructed signal is inputted as multi-channel observation signal into robust ICA to extract features of the source signal. Finally, the natural excitation technique and Hilbert transform are used to solve the self-oscillation frequency and damping ratio of the structure. The proposed method is validated using both simulation data and the GNSS monitoring data of the Wilford suspension bridge. The results show that the proposed method can effectively reduce the measurement noise and successfully identify the first-order vibration frequencies and damping ratios of bridge. This algorithm can also be applied in the parameter identification of other engineering structures from GNSS data.

Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

SummarySince bearing defects usually occur which threaten the stable operation of the machinery, bearing fault detection is of great importance. However, the bearing fault signals inevitably exhibit strong interference components due to the complex structures of the real equipment, which leads to difficulty in fault feature detection. To address the problem, a fault diagnosis method based on the ensemble average of enhanced generalized envelope spectrum (EAEGES) was constructed. First, to suppress the irrelevant components, the optimal analysis frequency band was determined based on the variable frequency band division criteria. Second, the optimal signal was divided into numerous sub‐signals, the EAEGES was established based on the principles of the generalized envelope spectrum to strengthen the capability in detecting bearing fault features, while the improved pulse extraction operator was utilized as the target. Finally, abundant fault information can be distinguished in the feature map acquired using the proposed technique. This technique shows high effectiveness in extracting the defect signatures of the rolling element bearing, which is demonstrated using the simulation and experimental signals and can be applied to real applications.

Power Quality Disturbance Detection Based on Partitioning-Improved Empirical Wavelet Transform

Power quality is an important indicator to measure the quality of power supply in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. Aiming at the detection of transient power quality disturbance, this paper proposes the empirical wavelet algorithm which improves the adaptive division. This paper improves the spectrum processing, frequency point selection and frequency band division to improve the adaptive division ability of the algorithm under complex perturbation signals. Combined with the Hilbert transform, a perturbation detection algorithm is proposed based on an improved empirical wavelet algorithm to extract the transient characteristics of the perturbation signal. Through software simulation, this method has good adaptive ability in the face of composite power mass transient disturbance and can accurately extract the transient characteristics of the signal.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Appendectomy in Management of Malrotation of Gut is it Necessary?

Malrotation is a congenital anatomical anomaly that affects the normal positioning of the intestines. Traditional management of malrotation, as described by Ladd, consists of detorsion of the volvulus if present, division of Ladd's bands, widening of the mesenteric root, proper positioning of the small and large bowels, and a prophylactic appendectomy. This study was done to determine whether appendectomy should be an integral part of the Ladds procedure or if it can be avoided. This retrospective observational study was conducted in one pediatric surgical unit in the tertiary care center of North India. All the cases of malrotation of the gut managed from January 2008 to December 2018 were reviewed. The details of the patients were recovered from the electronic data recording system of the hospital and manual operation theater records. The cases that have a follow-up of <5 years were not included in the study. The details were charted in an Excel Sheet for the analysis. No statistical test was performed because there was no event in patients in whom prophylactic appendectomy was not done. The data analysis revealed that a total of 66 malrotation patients were managed during this period, those who fulfilled the inclusion criteria. Among 66 cases included in the study, in 41 cases, prophylactic appendectomy was done, whereas in the rest 25, prophylactic appendectomy was not done. During the follow-up, none of the patients in whom the appendix was preserved presented with signs or symptoms related to appendicitis or any other complications. Ladds procedure without appendectomy can be performed in view of the potential use of the appendix in the future, and with the fact that in the current era of advanced medicine, appendicitis can be diagnosed early due to the advancement of imaging and better record keeping.

Migration of elastic optical networks to the C + L-bands subject to a partial upgrade of the number of erbium-doped fiber amplifiers

Space division multiplexing (SDM) and band division multiplexing (BDM) are considered promising technologies to increase the capacity of optical transport networks. The progressive shortage of available dark fibers and the immaturity of multicore and multimode fibers for multichannel transmission induce network operators to postpone the process of capacity enhancement through SDM. Therefore, capacity increase revolves around BDM by lighting up at least the L-band of the already installed optical fiber infrastructure, which is a practical solution in the short to middle term. However, L-band activation requires the upgrade of network components such as erbium-doped fiber amplifiers (EDFAs). To manage the imposed cost while leveraging the L-band, a network can be partially rather than fully migrated in a single step by upgrading just a subset of the fibers and thus a subset of EDFAs to operate in the C+L-bands. In this paper, the focus is set on determining which fibers in the network should be upgraded to exploit the L-band, subject to a constraint on the maximum number of EDFAs to be upgraded, and analyzing its impact on network performance when facing dynamic traffic in terms of the blocking ratio. To this end, three heuristic algorithms, each pursuing a different objective, and two of them based on an integer linear programming (ILP) formulation, are proposed for the network planning to identify which fibers to upgrade. Simulation results demonstrate that, thanks to the use of these heuristics, the upgrade of a partial set of links to the C+L line system is a viable solution for network operators to circumvent the huge cost associated with migrating the full network. For instance, we demonstrate that a strategic partial upgrade using the proposed methods, subject to upgrading a maximum of 60% of the EDFAs, can significantly boost the supported traffic load in the examined topologies, ranging from 175% to 322%, when compared to the non-upgraded network.

Decoding motor imagery based on dipole feature imaging and a hybrid CNN with embedded squeeze-and-excitation block

Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance has not been paid sufficient attention. In this paper, a novel dipole feature imaging (DFI) and a hybrid convolutional neural network (HCNN) with an embedded squeeze-and-excitation block (SEB), denoted as DFI-HCNN, are proposed for decoding MI tasks. EEG source imaging technique is used for brain source estimation, and each sub-band spectrum powers of all dipoles are calculated through frequency analysis and band division. Then, the 3D space information of dipoles is retrieved, and by using azimuthal equidistant projection algorithm it is transformed to a 2D plane, which is combined with nearest neighbor interpolation to generate multi sub-band dipole feature images. Furthermore, a HCNN is designed and applied to the ensemble of sub-band dipole feature images, from which the importance of sub-bands is acquired to adjust the corresponding attentions adaptively by SEB. Ten-fold cross-validation experiments on two public datasets achieve the comparatively higher decoding accuracies of 84.23% and 92.62%, respectively. The experiment results show that DFI is an effective feature representation, and HCNN with an embedded SEB can enhance the useful frequency information of dipoles for improving MI decoding.

Empirical mixed Ramanujan Fourier decomposition and its application to early fault diagnosis of planetary gears

The noise robustness of adaptive empirical Fourier decomposition (AEFD) is not ideal, and Ramanujan Fourier mode decomposition (RFMD) often occurs over decomposition, causing the fault information to be dispersed. Based on this, an original method called empirical mixed Ramanujan Fourier decomposition (EMRFD) is proposed for nonlinear and non-stationary signal analysis. Firstly, EMRFD obtains the coefficients of mixed Ramanujan Fourier transform (MRFT) through the new mixed transform matrix. Then, EMRFD obtains the partition boundary through adaptive frequency band division. Finally, the mode components named mixed Ramanujan Fourier intrinsic band functions (MRFIBFs) of frequency bands are obtained by inverse MRFT. The simulation and experimental signal analysis results show that EMRFD not only has excellent signal decomposition accuracy, but also has effective ability of extracting periodic components, which is a valid method for early fault diagnosis of planetary gears.

Comparison Between Clinical Outcome of Intralesional Human Placental Extract Alone and Topical Application of Placental Extract Gel After Fibrotomy in Oral Submucous Fibrosis.

Oral submucous fibrosis (OSMF) is a premalignant condition prevalent in our country. Juxtaepithelial inflammation with progressive hyalinization of the lamina propria results in stiffness and fibrosis of the oral mucosa, characterised by trismus, ankyloglossia, and a burning sensation. Various methods of treatment have been tried in these cases, which include placental extract injections and the cutting of fibrous bands. In this study, we aim to compare the outcome of intra-lesional placental extract injection with fibrotomy and placental extract gel application in OSMF. This prospective interventional study included 58 patients clinically diagnosed with OSMF grades II and III at a rural tertiary care hospital between January 2021 and August 2022. The patients were randomised into two groups: group I received 1 ml of intra-lesional human placental extract injection in the submucosal plane of the buccal mucosa and retro-molar trigone (RMT) once a week for five consecutive weeks, and group II was subjected to a transverse division of fibrotic bands in the submucosal plane under general anaesthesia. The surgical wound was left open, and swabs soaked in human-purified placental extract gel were placed in the wound for two hours twice daily until the surgical wound was epithelialized and healed. The patients in both groups I and II were advised to do jaw opening exercises, and weekly follow-up was done. Findings with regard to maximum mouth opening, colour of mucosa, and burning sensation in the oral cavity based on a Likert scale were documented. At the end of five months, the pre-treatment and post-treatment results documented were compared. All patients were between 20 and 60 years of age and were addicted to chewing areca nuts with tobacco. Bilateral involvement was present in all patients, with extension into the RMTand soft palate seen in 31%. Improvement in mouth opening was between 4 mm and 6 mm in group II, and relief of burning sensation and mucosal colour was better in group I. Intra-lesional placental extract injections help in the improvement of the mucosa and relief from the burning sensation. Fibrotomy with placental extract gel application is better at relieving trismus in OSMF. Aggressive mouth-opening exercises may improve mouth opening following the above procedures.

Power quality disturbance detection based on IEWT

With the continuous development of distributed generation, power quality problems are more complex. In order to ensure that the power quality of the active distribution network meets the requirements of users, it is of great significance to accurately obtain the power quality disturbance parameters. For the problem that the traditional empirical wavelet transform needs to manually select the number of frequency bands when analyzing unknown power quality disturbances, this paper proposes an improved empirical wavelet transform (IEWT) algorithm. The algorithm uses the Piecewise Cubic Hermite Interpolation (PCHIP) method and the improvement of the transition region to achieve adaptive frequency band division and noise interference reduction, and different amplitude (AM) and pitch (FM) components can be obtained. Then the Normalized Direct Quadrature (NDQ) algorithm and Singular Value Decomposition (SVD) algorithm are used to extract frequency, amplitude and time parameters of the AM-FM components. Simulation and experimental results show that the power quality disturbance parameters extracted by the proposed method are more accurate and less affected by noise.

Modal parameter identification in civil structures via Hilbert transform ensemble with improved empirical wavelet transform

To overcome the inaccurate frequency band division in empirical wavelet transform (EWT) induced by high noise and modulation edge band, this study proposes an improved EWT (IEWT) and applies it to operational modal analysis (OMA) in civil structures. The IEWT segments the frequency band using spectral trend determined with removing the high-frequency components of the Fourier spectrum. The synchroextracting transform is employed to denoise the frequency components obtained from IEWT combined with the random decrement technique and Hilbert transform to identify modal parameters. The effectiveness of IEWT and the proposed modal analysis method is comprehensively verified by a full-scale cable-stayed footbridge. The results demonstrate that the proposed IEWT, random decrement technique, and Hilbert transform can accurately identify the modal parameters from multi-setup ambient vibration measurements.