Hilbert Spectrum Research Articles

Automatic detection method of abnormal vibration of engineering electric drive construction machinery

<abstract> <p>Aiming at the problem that the extraction effect of abnormal vibration characteristics of current engineering electric drive construction machinery is poor, an automatic detection method of abnormal vibration of engineering electric drive construction machinery is proposed. Firstly, the abnormal data of mechanical abnormal vibration are collected and identified, and based on the identification results, the dynamic characteristic model of engineering electric drive construction machinery is constructed. The empirical mode decomposition and Hilbert spectrum are used to decompose the abnormal vibration of machinery, calculate the response amplitude and time lag value generated by the operation of the engineering electric drive construction machinery to simplify the diagnosis steps of the abnormal vibration of the engineering electric drive construction machinery and realize the positioning and detection of the transverse and torsional vibration characteristics. Finally, through experiments, it was confirmed that the automatic detection method of the abnormal vibration of the engineering electric drive construction machinery has high accuracy, which can better ensure the healthy operation of mechanical equipment. This endeavor aims to establish scientific methodologies and standards for fault detection techniques in construction machinery, ultimately forging a versatile solution better suited for detecting and resolving issues across various categories of construction equipment.</p> </abstract>

An effective Hilbert-Huang transform-based approach for dynamic eccentricity fault diagnosis in double-rotor double-sided stator structure axial flux permanent magnet generator under various load and speed conditions

Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert-Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against load and speed variations.

Vibracije bloka motora - indikator kucanja u SI motoru

The factors influencing the onset of knocking have a significant impact on how well a SI engine performs. Hence, the efficacy in determining the onset and controlling of knock is a key factor in improving the SI engine's performance. This paper provides insight into the role of engine block vibrations in determining the occurrence of knock using Empirical Mode Decomposition and Short-Time Fourier Transform. To comprehend the behaviour of vibration amid normal combustion and knocking conditions, the engine block vibration signals are analyzed and compared with the incylinder pressure fluctuations. The features of knock are extracted from the engine block vibration signals using Empirical Mode Decomposition. The first Intrinsic Mode Function (IMF) thus obtained is used to generate the Hilbert spectrum for detecting the occurrence of knock. Similarly, ShortTime Fourier Transform is also performed on the first IMF to obtain the spectrogram. The findings demonstrate unequivocally that higher frequency variations are produced when knock occurs. These results also indicate that the combination of Empirical Mode Decomposition and Short-Time Fourier Transform can be used effectively for determining the occurrence of knock.

Hilbert–Huang Transform Incorporating Analytical Mode Decomposition and Its Application in Fractured-Vuggy Carbonate Reservoir Prediction

High-precision time-frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this paper, a TFA method based on analytical mode decomposition (AMD) and the Hilbert-Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data.

Analysis of wave-induced vertical ship responses by Hilbert-Huang transform method

Time series of wave-induced ship responses are analysed by the Hilbert-Huang Transform (HHT) method. The results of the analysis of numerically simulated motions and loads on a chemical tanker in head waves are compared with the analysis of experimental data from laboratory model tests. The Empirical Mode Decomposition, which is a first part of HHT method is applied to the sea surface elevations of two sea states (high and low seas) and the wave-induced vertical motions and bending moments and the results are discussed. It is found that the decomposition of the experimental data into intrinsic components compares well with the decomposition of the numerically simulated ones for both high and low seas. The first two or three intrinsic mode functions carry the major part of the energy of both experimental and numerical simulated data. The time-frequency distribution of energy, called Hilbert spectrum, is calculated by the second part of HHT method. The Hilbert spectrum of the sea surface elevation is compared with Hilbert spectrum of wave-induced ship motions and moments in an attempt to study the time-frequency characteristics of ship response. The results of the present work provide confidence in the usage of numerical simulation data for the decomposition into intrinsic components of ship responses, avoiding the need for experimental data from laboratory model tests.

Dynamic damage identification of tunnel portal and verification via shaking table test

Tunnel portals are seismically weak. Large-scale shaking-table tests of a tunnel portal were conducted. A method based on the Hilbert-Huang transform (HHT) for dynamic damage identification of tunnel portals was studied using the test results. The test preparation process and basic principles of the HHT are briefly described. The intrinsic-mode functions (IMFs) after empirical mode decomposition (EMD) were evaluated using multiple indicators, including the coefficient of determination (R2), root-mean-square error (Rmse), variance contribution rate (Mi), and energy moment (E). The time histories of the IMFs, marginal Hilbert spectrum, and Hilbert instantaneous energy spectrum were analysed. The results show that the damage development process of the lining and slope can be characterised by the marginal Hilbert spectrum and Hilbert Instantaneous energy spectrum. The slope had an obvious acceleration amplification effect with elevation and surface-trend amplification effect. Damage to the tunnel-portal slope is mainly caused by the development of tension cracks at the top of the slope, falling of scattered particles on the slope shoulder, development of cracks on the slope surface, and instability and collapse of the surrounding rock near the tunnel portal. Slope surface damage developed faster and to a greater extent than the interior of the slope. The lining of the portal section was dominated by longitudinal cracks, with some diagonal cracks. Damage to the invert at the tunnel portal occurs earlier than that of the crown, and the degree of injury is greater than that of the crown. The degree of damage and development speed of the tunnel portal were also greater than those of the tunnel body. The proposed method can serve as a guide for identifying the dynamic tunnel-portal damage.

Stochastic non-stationary characteristics of vehicle-induced bridge vibrations

Quantifying vehicle-bridge interaction mechanism is critical for bridge safety design, transportation weight control, condition assessments in maintenance, and structural health monitoring. In this paper, the stochastic non-stationary characteristics of a bridge under vehicle-bridge interaction are investigated. The response of the bridge under the loading of vehicles is, respectively, depicted in Fourier spectrum, spectrogram, and Hilbert spectrum. Both Fourier spectrum and spectrogram exhibit some cluster bands. The Hilbert spectrum exhibits time-varying frequencies in the proximity of low bridge natural modes, which suggests possible non-linear and non-stationary characteristics. The stochastic and non-stationary properties are further characterized by using bispectrum and recurrence analysis, and the weak nonlinear properties are characterized by using nonlinear invariants. The free-decay response of bridge (vehicle left the bridge) exhibits linear properties, which is correlated with the natural modes of bridge, whereas the bridge vibrations under moving vehicle exhibit complicated stochastic non-stationary and non-linear properties. These provide the insights into the conventional modal analysis-based approaches for heavy vehicle-loaded bridge condition assessments and structural health monitoring.

Application of Hilbert Spectrum based on SWT to Low-Frequency Oscillation Analysis

Low-frequency oscillation (LFO) appears in power systems with fast excitation of a unit or large-scale connections. An LFO signal exhibits typical nonstationary and time-varying characteristics. The paper proposed an LFO analyzing technique through the Hilbert spectrum of stationary wavelet transform (SWT). The SWT-based Hilbert spectrum contains time-varying and frequency information of LFO. The analytic framework is introduced, SWT is formulated to decompose an LFO signal into the sum of several coefficient components, the instantaneous parameters of each coefficient are then individually calculated, the complete time-frequency distribution is combined, and finally, the time-varying oscillations characteristics parameters are extracted. The simulation shows that the proposed method effectively reveals time-frequency features of the LFO signal.

Vibration Behavior of Dual-Rotor Caused by Maneuver Load and Intershaft Bearing Defect

This paper concentrates on the vibration amplitude modulation and frequency modulation behavior of a dual-rotor system induced by the maneuver load and defective intershaft bearing. The motion equations of a dual-rotor system are presented by considering the intershaft bearing force with a local defect of the outer raceway and the maneuver load of a barrel-roll flight. The fourth-order Runge–Kutta method is employed to study the vibration responses of the dual-rotor system, which are reflected by the time history curves, the bearing contact force curves, and the Hilbert envelope spectra. It is shown that the rotor responses display impulse vibrations due to the defective intershaft bearing; and the corresponding Hilbert envelope spectra contain defect characteristic frequencies, multiple defect frequencies, side frequencies, and modulation frequencies. The results reveal the frequency modulation behavior of the impulse vibrations induced by the rotating speed ratio and the amplitude modulation behavior influenced by the maneuver load, the bearing defect span, and the rotating speed. Finally, defective intershaft bearing experiments under maneuvering flight are conducted, confirming the availability of the numerical vibration amplitude modulation and frequency modulation behavior. This analysis provides a theoretical foundation for bearing state detection during flight maneuvers.

Milling chatter recognition based on dynamic and wavelet packet decomposition

Abstract. In metal milling, especially in the machining of low-stiffness workpieces, chatter is a key factor affecting many aspects such as surface quality, machining efficiency, and tool life. In order to avoid chatter, a milling chatter identification method based on dynamic wavelet packet decomposition (WPD) is proposed from the perspective of signal processing. The dynamic characteristics of the system are obtained by a hammer test. Based on the principle that the chatter frequency will reach a peak value near the natural frequency of the system, the original milling force signal is decomposed by WPD, and the sub-signals containing rich chatter information are selected for signal reconstruction. After numerical analysis and spectrum comparison, the reconstruction scheme is proved to be robust. Then, the time–frequency domain image of the reconstructed signal and the Hilbert spectrum feature are compared and analyzed to identify the chatter. Finally, the validity and reliability of the proposed method for chatter recognition are verified by experiments.

Research on the Identification Method of Overhead Transmission Line Breeze Vibration Broken Strands Based on VMD−SSA−SVM

For the problem of overhead line broken strand identification, a VMD−SSA−SVM-based overhead transmission line broken strand identification method is proposed. First, the vibration acceleration response signal of overhead transmission line under breeze environment was simulated by Ansys simulation software. Then the variable modal decomposition of the vibration acceleration response of the transmission line is performed to obtain the intrinsic mode function corresponding to the vibration behavior of the transmission line. The Hilbert transform is used to analyze the intrinsic mode functions of each order, and the Hilbert spectrum and marginal spectrum of the intrinsic mode functions of each order are studied to extract the first six order intrinsic frequencies. Each set of intrinsic frequencies is input as a feature vector into a support vector machine optimized by the sparrow search algorithm for training to achieve the identification of broken strands in overhead transmission lines. This paper studies the inherent frequency changes and identification results of overhead transmission lines under two states of normal operation and broken 5 strands operation, and the test results show that the identification method based on VMD-SSA-SVM has good identification effect on broken strands of overhead lines.

Interpreting electrochemical noise signal arising from stress corrosion cracking of 304 stainless steel in simulated PWR primary water environment by coupling acoustic emission

Electrochemical noise (EN) signals arising from stress corrosion cracking (SCC) of 304 stainless steel (SS) in borated and lithiated high-temperature water were monitored. Acoustic emission (AE) signals were simultaneously measured to relate the EN signals with the SCC process. Through Weibull analysis of the EN signals, it was demonstrated that the EN technique can in-situ capture the SCC events in high-temperature water. With evolution of the SCC process, Hilbert time-frequency spectrum of the EN signals gradually migrated from high frequency to low frequency. The capacity of EN and AE techniques to monitor the SCC process in high-temperature water is compared.

Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

Hilbert‐Huang Transform Analysis of the Concentration Field of a Two‐Layer Impinging Stream Mixer

Hilbert‐Huang Transform Analysis of the Concentration Field of a Two‐Layer Impinging Stream Mixer

Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

Recently, deep learning has become the mainstream solution to solve specific emitter identification (SEI) problems. However, because large amounts of labeled signal samples cannot be obtained in noncooperative scenarios, the performance of deep learning-based data-driven methods for SEI was limited. As a result, a novel SEI method targeted on few-shot was proposed in this study. First, the received signal was preprocessed based on variational mode decomposition and the Hilbert analysis to obtain the Hilbert time-frequency spectrum. Subsequently, a classification neural network model was built and trained with a small number of Hilbert time-frequency spectrum samples through meta-learning. This model could identify specific emitters with limited training samples. The experimental results showed that this method accomplishes network training with as few as 80 training samples while obtaining a good level of generalization and effectively identifying different emitter individuals. In addition, this method exhibits a strong robustness to noise by maintaining an identification accuracy of more than 80% in channels with low signal-to-noise ratios. Finally, the proposed method demonstrated better identification performances than other existing methods with its capability to effectively solve SEI problems in the few-shot scenario.

MEMD-HHT based Emotion Detection from EEG using 3D CNN.

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.

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.

Remaining Useful Life Estimation of Cooling Units via Time-Frequency Health Indicators with Machine Learning

Predictive Maintenance (PM) strategies have gained interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. Taking advantage of condition monitoring data from aircraft systems, Prognostics and Health Maintenance (PHM) practitioners have been predicting the life span of aircraft components by applying Remaining Useful Life (RUL) concepts. Additionally, in prognostics, the construction of Health Indicators (HIs) plays a significant role when failure advent patterns are strenuous to be discovered directly from data. HIs are typically supported by data-driven models dealing with non-stationary signals, e.g., aircraft sensor time-series, in which data transformations from time and frequency domains are required. In this paper, we build time-frequency HIs based on the construction of the Hilbert spectrum and propose the integration of a physics-based model with a data-driven model to predict the RUL of aircraft cooling units. Using data from a major airline, and considering two health degradation stages, the advent of failures on aircraft systems can be estimated with data-driven Machine Learning models (ML). Specifically, our results reveal that the analyzed cooling units experience a normal degradation stage before an abnormal degradation that emerges within the last flight hours of useful life.

Aircraft Engine Surge-Precursor Detection and Antisurge Control Method at Onboard Environment

This paper is concerned with a new detection method to realize the real-time judgment of surge precursor by the low-sampling-rate compressor discharge static pressure at onboard environment, and it provides the possibility for antisurge control. Firstly, an improved Hilbert–Huang transform time-frequency analysis algorithm is proposed to get the Hilbert spectrum of the windowed signals in real time. The surge-precursor intensity (SPI) is characterized by counting the signal amplitude in the low-frequency range. The surge early warning signal is judged according to whether the SPI crosses the threshold. Then, this paper handles the misjudgment problem caused by the endpoint effect phenomenon in the detection algorithm by adding data segments at the beginning and end of the analysis signal. Finally, to prevent the possible real-time jump phenomenon of the surge warning signal during the detection process, a judgment logic module is added. The verification results of multiple sets of experimental data showed that the warning time margin is . This paper also established an antisurge control system. The results verified that the detection method is effective, and the time margin of the early warning signal judged meets the response time requirements of the control system.

Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper.