Frequency Band Division Research Articles

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.

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.

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.

Normalized feature energy-guided adaptive frequency band division method for wheelset bearing compound fault detection

Normalized feature energy-guided adaptive frequency band division method for wheelset bearing compound fault detection

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.

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.

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.

A Multi-Feature Fusion Speech Emotion Recognition Method Based on Frequency Band Division and Improved Residual Network

A Multi-Feature Fusion Speech Emotion Recognition Method Based on Frequency Band Division and Improved Residual Network

On‐load tap‐changer fault mode recognition based on the singular value of Hilbert energy spectrum time‐frequency matrix and spectrum entropy

To solve the difficulty of eigenvalue extraction of on-load tap-changer (OLTC) vibration signals, an eigenvalue extraction method of time-frequency matrix based on Hilbert energy spectrum and spectral entropy is proposed in this study. By optimizing the Hilbert-Huang transform (HHT) algorithm, the Hilbert energy spectrum of the vibration signal based on the time-frequency distribution is obtained. The spectrum entropy of Hilbert energy spectrum was extracted to characterize the chaotic degree of vibration signal energy distribution, and avoided the problem of inaccurate feature quantity such as similarity due to the minute time difference during signal acquisition. A new time-frequency matrix was constructed by frequency band division and singular value decomposition (SVD) was carried out. The singular value vector obtained contains the time, frequency and energy information, such as vibration frequency distribution and time-frequency plane energy distribution, which represent the essential features of the original signal. The mechanical states of OLTC were characterized by Hilbert spectrum entropy and singular value. The features of the measured signals under the three typical faults of the driving mechanism are input into the intelligent multi-classification support vector machine (DAG-SVM) for pattern classification. The results show that this method can accurately identify the mechanical state of OLTC, and has practical application significance.

A novel optimal demodulation frequency band extraction method of fault bearing based on power spectrum screening combination-gram

As the prerequisite for obtaining the optimal demodulation frequency band (ODFB), the frequency band division model must be delicately constructed. The division models in existing methods of obtaining ODFB are vulnerable to the background noise and irrelevant components, which will cause an unsuitable demodulation frequency band. To improve the performance of frequency band division model, this paper proposes a fault diagnosis method called power spectrum screening combination-gram (Psscgram). Firstly, a frequency band screening combination model based on the auto-regressive (AR) power spectrum is established to obtain the frequency band group of vibration signal. Secondly, the negative entropy (NE) is used to quantify the fault characteristics of the frequency band group. The frequency band with the largest NE value is extracted from the frequency band group, whose characteristic frequency is calculated via envelope spectrum. Finally, the robustness and the anti-interference of the proposed method is verified by simulated signals with different signal-to-noise ratio. Moreover, the ODFB extraction ability of the proposed method is proved by the analyses of three sets of bearing experimental signals.

Median line-gram and its application in the fault diagnosis of rolling bearing

The level selection of frequency band division structure relies on prior information in most gram approaches that capture the optimal demodulation frequency band (ODFB). When an improper level is specified in these approaches, the fault characteristic information contained in the produced ODFB may be insufficient. This study proposes a unique approach, termed median line-gram, to tackle the level selection problem. To divide the frequency domain signal into a series of demodulation frequency bands, a spectrum median-line segmentation model based on Akima interpolation is first created. The level and boundary of the segmentation model can be adaptively identified by this means. Second, the acquired frequency bands are quantized using the negative entropy index, and the ODFB is defined as the frequency band with the largest value. Third, the envelope spectrum is used to determine the ODFB characteristic frequency to pinpoint the bearing fault location. Finally, both simulation and experimental signal analysis are used to demonstrate the efficiency of the suggested method. Furthermore, the proposed method extracts more defect feature information from the ODFB than existing methods.

Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.

Generalized Variational Mode Decomposition: A Multiscale and Fixed-Frequency Decomposition Algorithm

To overcome the limitations of variational mode decomposition (VMD) algorithm that its frequency scales and spectrum positions cannot be flexibly adjusted to decompose signals as required, a generalized VMD (GVMD) was proposed. This article addresses the fundamental theory of GVMD. In order to highlight the local characteristics of the signal much more while considering its global data fidelity, a set of variational models is formed, where individual constrained optimization problem is constructed for each mode. The formed variational models are solved by the modified alternating direction method of multipliers approach, thus realizing the multiscale and fixed-frequency decomposition. To gain a deep insight into GVMD algorithm, its frequency band division manner is investigated. In essence, GVMD can be viewed as a bank of filters whose bandwidths and center frequencies can be flexibly adjusted by its parameters, i.e., scale parameters and prior center frequencies. The effectiveness of GVMD is verified on simulated and real signals. The preliminary results show that compared with state-of-the-art methods, GVMD can make full use of feature information to decompose original signals as desired into several narrowband modes or into several narrowband modes and a wideband mode, effectively obtaining the interested modes.

Odor-induced emotion recognition based on average frequency band division of EEG signals

BackgroundEmotion recognition plays a key role in multimedia. To enhance the sensation of reality, smell has been incorporated into multimedia systems because it can directly stimulate memories and trigger strong emotions. New methodFor the recognition of olfactory-induced emotions, this study explored a combination method using a support vector machine (SVM) with an average frequency band division (AFBD) method, where the AFBD method was proposed to extract the power-spectral-density (PSD) features from electroencephalogram (EEG) signals induced by smelling different odors. The so-called AFBD method means that each PSD feature was calculated based on equal frequency bandwidths, rather than the traditional EEG rhythm-based bandwidth. Thirteen odors were used to induce olfactory EEGs and their corresponding emotions. These emotions were then divided into two types of emotions, pleasure and disgust, or five types of emotions that were very unpleasant, slightly unpleasant, neutral, slightly pleasant, and very pleasant. ResultsComparison between the proposed SVM plus AFBD method and other methods found average accuracies of 98.9 % and 88.5 % for two- and five-emotion recognition, respectively. These values were considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods. ConclusionsThe SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition. Classification of the five-emotion categories was generally inferior to the classification of the two-emotion categories, suggesting that the recognition performance decreased as the number of emotions in the category increased.