Bispectrum Analysis Research Articles

Fault feature extraction of localised rub-impact fault based on vector-bispectrum and Teager energy operator

Bispectrum analysis is able to suppress the Gaussian noise and analyse quadratic phase coupling in the signals more accurately. However, traditional bispectrum has the problem of losing fault information at low frequency, and may not comprehensively reflect nonlinear information of the fault, which leads to weak diagnostic ability, especially for local rub-impact fault. For this reason, a new feature extraction method called vector-bispectrum energy operator is presented in this paper, which adopts vector-bispectrum based on Teager energy operator and is used to obtain the fault feature of local rubbing rotor. The results of the experiment show that the feature of the local rubbing fault extracted by Teager energy operator based vector-bispectrum is very noticeable, and the fault classification through SVM has a high recognition rate.

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

In the bearing health assessment issues, using the adaptive nonstationary vibration signal processing methods in the time-frequency domain, lead to improving of early fault detection. On the other hand, the noise and random impulses which contaminates the input data, are a major challenge in extracting fault-related features. The main goal of this paper is to improve the Ensemble Empirical mode decomposition (EEMD) algorithm and combine it with a new proposed denoising process and the higher order spectra to increase the accuracy and speed of the fault severity and type detection. The main approach is to use statistical features without using any dimension reduction and data training. To eliminate unrelated components from faulty condition, the best combination of denoising parameters based on the wavelet transform, is determined by a proposed performance index. In order to enhance the efficiency of the EEMD algorithm, a systematic method is presented to determine the proper amplitude of the additive noise and the Intrinsic Mode Functions (IMFs) selection scheme. The fault occurrence detection and the fault severity level identification are performed by the Fault Severity Index (FSI) definition based on the energy level of the Combined Fault-Sensitive IMF (CFSIMF) envelope using the central limit theorem. Also, taking the advantages of a bispectrum analysis of CFSIMF envelope, fault type recognition can be achieved by Fault Type Index (FTI) quantification. Finally, the proposed method is validated using experimental data set from two different test rigs. Also, the role of the optimum denoising process and the algorithm of systematic selection of the EEMD parameters are described regardless of its type and estimating the consistent degradation pattern.

Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis.

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum

Engine knock limits the thermal efficiency improvement of spark-ignition (SI) engines. Thus, the extract research of the knock characteristics has a great significance for the development of gasoline engines. The research proposes a novel knock detection and diagnosis method in SI engines using the CEIITD (Complementary Ensemble Improved Intrinsic time-scale decomposition) and Bi-spectrum algorithm. The CEIITD algorithm is used to extract the knock characteristics. The results show that the CEIITD algorithm can effectively and clearly extract the knock shock characteristics (including light knock) through the vibration signals. A Bi-spectrum analysis can further distinguish between the light knock signal and normal combustion signal. The Bi-spectrum results also show that knock characteristic has a strong non-Gaussian property. At last, the Band pass filter and Improved ITD method were employed to identify the knock characteristics from these cylinder block vibration signals. The comparison result shows that the CEIITD method proposed in this paper is more suitable to detect the knock characteristic.

Pedestal dynamics across low to high confinement regime in the HL-2A tokamak

Interactions among pedestal shear flows, turbulence, and the formation of the edge transport barrier have been studied in H-mode plasmas of the HL-2A tokamak by multi-channel Doppler reflectometry with high spatiotemporal resolution. Geodesic acoustic mode (GAM) has been observed during the L-I-H transition. It has been observed that the plasma transits into the I-phase when the mean E×B shear flow reaches a critical value. The bi-spectrum analysis has shown that there is a strong interaction between GAM and limit cycle oscillation (LCO), and the energy transfer is from GAM to LCO, suggesting that GAM can assist the L-I transition. The regulation of the edge turbulence by LCOs helps to build the steep pedestal and initialize the confinement improvement of the plasma. It has been found that the mean E×B shear flow is further increased just before the I-H transition, accompanied by the turbulence suppression, leading to the edge transport reduction and the pedestal formation. It has been demonstrated that the increase of the mean E×B shear flow prior to the L-I and I-H transitions is due to the ion diamagnetic component of Er. These results corroborate that the mean E×B shear flow plays a key role in the L-I and I-H transitions.

A Sparse Modulation Signal Bispectrum Analysis Method for Rolling Element Bearing Diagnosis

Modulation signal bispectrum (MSB) analysis is an effective method to obtain the fault frequency for rolling bearing, but harmonics make fault frequency dense and even frequency aliasing. Carrier frequency of bearing is generally determined by its structure and inherent characteristics and changes with the increase of the damage degree, so it is hard to be accurately found. To solve these problems, this paper proposes a sparse modulation signal bispectrum analysis method. Firstly the vibration signal is demodulated by MSB analysis and its bispectrum is obtained. After the frequency domain filtering, the carrier frequency is computed based on the characteristics of energy concentration at the carrier frequency on MSB. By shift-frequency MSB (SF-MSB), the carrier frequency is moved to the coordinate origin, the entire MSB is shifted for the same distance, and SF-MSB is obtained. At last, the bispectrum is shifted to the frequency zero point and diagonal slices are performed to obtain a sparse representation of MSB. Experimental results show that sparse MSB (S-MSB) method can not only eliminate the interference of harmonic frequency, but also make the extracted characteristic frequency of fault more obvious.

Imaging through a thin scattering layer and jointly retrieving the point-spread-function using phase-diversity.

Recently introduced angular-memory-effect based techniques enable non-invasive imaging of objects hidden behind thin scattering layers. However, both the speckle-correlation and the bispectrum analysis are based on the statistical average of large amounts of speckle grains, which determines that they can hardly access the important information of the point-spread-function (PSF) of a highly scattering imaging system. Here, inspired by notions used in astronomy, we present a phase-diversity speckle imaging scheme, based on recording a sequence of intensity speckle patterns at various imaging planes, and experimentally demonstrate that in addition to being able to retrieve the image of hidden objects, we can also simultaneously estimate the pupil function and the PSF of a highly scattering imaging system without any guide-star nor reference.

Research on Bearing Fault Diagnosis Based on Slice Spectrum and Cepstrum

In this paper, high-order spectral analysis and cepstrum analysis are combined to diagnosis of bearing failure. The high-order spectrum can effectively suppress the Gaussian noise and extract the weak fault signal, but it has the limitation when dealing with the added signal. The cepstrum, which less affected by the transmission path, can distinguish the periodic components in the sideband and simplify the clustered sideband into a single spectral line. In this paper, the two methods are combined to conduct the bispectrum analysis of the vibration signal, and then do the Inverse Fourier transform to the logarithm of the bispectrum model, that is, the cepstrum analysis of the third-order cumulant. The method proposed in this paper is verified by the vibration signal data of a bearing, and the results show that the method proposed in this paper can overcome the shortcomings of the traditional single method and make effective diagnosis of the bearing failure.

Combined analysis of galaxy cluster number count, thermal Sunyaev-Zel’dovich power spectrum, and bispectrum

The thermal Sunyaev-Zel’dovich (tSZ) effect is a powerful probe of the evolution of structures in the universe, and is thus highly sensitive to cosmological parameters σ8 and Ωm, though its power is hampered by the current uncertainties on the cluster mass calibration. In this analysis we revisit constraints on these cosmological parameters as well as the hydrostatic mass bias, by performing (i) a robust estimation of the tSZ power-spectrum, (ii) a complete modeling and analysis of the tSZ bispectrum, and (iii) a combined analysis of galaxy clusters number count, tSZ power spectrum, and tSZ bispectrum. From this analysis, we derive as final constraints σ8 = 0.79 ± 0.02, Ωm = 0.29 ± 0.02, and (1−b) = 0.71 ± 0.07. These results favor a high value for the hydrostatic mass bias compared to numerical simulations and weak-lensing based estimations. They are furthermore consistent with both previous tSZ analyses, CMB derived cosmological parameters, and ancillary estimations of the hydrostatic mass bias.

Excitation of internal m = 1 mode during application of resonant magnetic perturbations on J-TEXT tokamak

The excitation of internal m = 1 mode during application of resonant magnetic perturbations (RMPs) is observed on J-TEXT tokamak. It is found that the sawtooth oscillation disappears after RMPs penetration, and subsequently an internal m = 1 mode with a frequency around 2 kHz appears, were m and n are the poloidal and toroidal mode numbers, respectively. In addition, the internal m = 1 mode often coexists with a rotating m/n = 2/1 tearing mode, and its frequency increases by about 0.5 kHz when the 2/1 tearing mode is locked by RMPs. The bispectrum analysis proves that the m = 1 mode interacts with the rotating 2/1 tearing mode, which implies the mode coupling between these two modes. The frequency of m = 1 mode increases for higher electron density. These results reveal that, the internal m = 1 mode can be excited by RMPs and coexist with both locked and rotating 2/1 mode due to toroidal mode coupling.

A general relativistic signature in the galaxy bispectrum: the local effects of observing on the lightcone

Next-generation galaxy surveys will increasingly rely on the galaxy bispectrum to improve cosmological constraints, especially on primordial non-Gaussianity. A key theoretical requirement that remains to be developed is the analysis of general relativistic effects on the bispectrum, which arise from observing galaxies on the past lightcone, as well as from relativistic corrections to the dynamics. As an initial step towards a fully relativistic analysis of the galaxy bispectrum, we compute for the first time the local relativistic lightcone effects on the bispectrum, which come from Doppler and gravitational potential contributions. For the galaxy bispectrum, the problem is much more complex than for the power spectrum, since we need the lightcone corrections at second order. Mode-coupling contributions at second order mean that relativistic corrections can be non-negligible at smaller scales than in the case of the power spectrum. In a primordial Gaussian universe, we show that the local lightcone projection effects for squeezed shapes at z ∼ 1 mean that the bispectrum can differ from the Newtonian prediction by ≳ 10% when the short modes are k ≲ (50 Mpc)−1. These relativistic projection effects, if ignored in the analysis of observations, could be mistaken for primordial non-Gaussianity. For upcoming surveys which probe equality scales and beyond, all relativistic lightcone effects and relativistic dynamical corrections should be included for an accurate measurement of primordial non-Gaussianity.

Bispectrum and Energy Analysis of Wind Speed Data

Bispectrum and Energy Analysis of Wind Speed Data

Modulation signal bispectrum analysis of electric signals for the detection and diagnosis of compound faults in induction motors with sensorless drives

ABSTRACTAs a prime driver, induction motor is the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal status and avoid excessive energy consumption and severe damages to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar (BRB) faults under closed loop operation modes. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives a more correct indication of the fault severity and its location for all operating conditions.

MFSK Signal Individual Identification Algorithm Based on Bi-spectrum and Wavelet Analyses

MFSK Signal Individual Identification Algorithm Based on Bi-spectrum and Wavelet Analyses

Single-shot diffraction-limited imaging through scattering layers via bispectrum analysis.

Recently introduced speckle correlations-based techniques enable noninvasive imaging of objects hidden behind scattering layers. In these techniques, the hidden object Fourier amplitude is retrieved from the scattered light autocorrelation, and the lost Fourier phase is recovered via iterative phase-retrieval algorithms, which suffer from convergence to wrong local minimums solutions and cannot solve ambiguities in object orientation. Here, inspired by notions used in astronomy, we experimentally demonstrate that in addition to Fourier amplitude, the object-phase information is naturally and inherently encoded in the scattered light bispectrum (the Fourier transform of triple correlation) and can also be extracted from a single high-resolution speckle pattern, based on which we present a single-shot imaging scheme to deterministically and unambiguously retrieve diffraction-limited images of objects hidden behind scattering layers.

Research on bispectrum analysis of secondary feature for vehicle exterior noise based on nonnegative tucker3 decomposition

Research on bispectrum analysis of secondary feature for vehicle exterior noise based on nonnegative tucker3 decomposition

Multiple Feature Extraction of Electroencephalograph Signal for Motor Imagery Classification through Bispectral Analysis

Electroencephalograph (EEG) signals associated with motor imagery (MI) are highly non-Gaussian, non-stationary and have non- linear characteristics. Bispectral analysis is an advanced signal processing technique that quantifies quadratic non-linearities (phase-coupling) among the components of a signal and holds promise for characterizing MI-related EEG. Studies have been reported on the applicability of bispectrum for MI classification; often with different choice of high order spectra features. Question remains as to which of the different features of non-linear interactions over frequency components are best suited for MI classification. In this paper, an analysis based on bispectrum is reported to extract multiple high order spectra features of EEG for MI classification. MI signals from C3 and C4 channels for two tasks are used in the analysis. Based on bispectrum analysis, four high order spectra features are extracted. The classification results indicate that the extracted features could differentiate the two MI tasks with an accuracy of 90±4.71%.

An Approach to Fault Diagnosis for Gearbox Based on Image Processing

The gearbox is one of the most important parts of mechanical equipment and plays a significant role in many industrial applications. A fault diagnostic of rotating machinery has attracted attention for its significance in preventing catastrophic accidents and beneficially guaranteeing sufficient maintenance. In recent years, fault diagnosis has developed in the direction of multidisciplinary integration. This work addresses a fault diagnosis method based on an image processing method for a gearbox, which overcomes the limitations of manual feature selection. Differing from the analysis method in a one-dimensional space, the computing method in the field of image processing in a 2-dimensional space is applied to accomplish autoextraction and fault diagnosis of a gearbox. The image-processing-based diagnostic flow consists of the following steps: first, the vibration signal after noise reduction by wavelet denoising and signal demodulation by Hilbert transform is transformed into an image by bispectrum analysis. Then, speeded up robustness feature (SURF) is applied to automatically extract the image feature points of the bispectrum contour map, and the feature dimension is reduced by principal component analysis (PCA). Finally, an extreme learning machine (ELM) is introduced to identify the fault types of the gearbox. From the experimental results, the proposed method appears to be able to accurately diagnose and identify different types of faults of the gearbox.

Application of bispectral analysis of vibroacoustic signals for diagnosis blades cracks in aircraft engines

Background . The work is devoted to vibroacoustical diagnosis of the crack-like damages of the gas-turbine engines (GTE) blades at the steady-state modes of GTE. Objective . The effectiveness of the usage bispectrum analysis is investigated for signal processing at the vibroacoustical diagnostic of the blades cracks of aviation gas-turbine engines. Methods . Bispectral analysis is used for diagnostic information processing. Bispectrum can be found using Fourier transform third-order autocorrelation functions: \[S_{3x}(f_{1},f_{2})=\sum_{k,l=-\infty }^{\infty}C_{3x}(k,l)\textrm{exp}(-j2\pi f_{1}k)\textrm{exp}(-j2\pi f_{2}l).\] Results . Simulation bispectrum analysis vibroacoustical signals, radiated by models blades of working wheels without crack and with crack at the stationary vibrating excitations are carried out and investigated. It demonstrates that appearence and evolution of the fatigue crack in a blade increases intensity of bispectral modules. Conclusions . Application of a modern signal processing methods allows to detect fault features, which are sensitive to small crack-like damages. The receive results can be used to create a vibroacoustical monitoring system for aircraft engine rotor components.

Application of the state deterioration evolution based on bi-spectrum entropy and HMM in wind turbine

Concerning the problem of large rotating machinery with non-stationary state like wind turbine, this research mainly makes an emphasis on the method of state deterioration recognition based on bi-spectrum entropy and HMM (Hidden Markov Model). Firstly, the true signal such as low-speed start vibration signals of rotor test rig in the normal state and a plurality of imbalance deterioration degrees are collected. Bi-spectrum is applied to obtain the fault feature from the vibration signals mixed with a complex background noise. On the basis of bi-spectrum analysis, a bi-spectrum entropy algorithm is derived under the condition of subspace distribution probability, and the HMM for the fault pattern recognition is established by using the bi-spectrum entropy feature as input. This method is verified by successfully recognizing four state deterioration degrees. Finally, the method is applied to recognize the imbalance deterioration degree of wind turbine with the type of SL1500/82 and equipment actual working condition verified the effectiveness of the proposed method.