Signal Singularity Research Articles

The Research of Blast Furnace Blower Shaft Fault Diagnosis Beased on Wavelet Analysis

This article briefly introduces that the blast blower shaft fault diagnosis and prediction based on wavelet analysis. When the blast furnace operation of axial fan of vibration signal de-noising and singularity analysis, the fan operation state for real-time monitoring, timely find fault information and fault point, guarantee the safety, stability of the blast blower operation. Through the data with Matlab simulation effective, is that the wavelet analysis used in blast furnace fan axis of the fault diagnosis of feasibility.

Wavelet Selection in Fault Diagnosis of Wavelet Transform

Wavelet analysis is a kind of time-frequency analysis method, It is particularly effective to analyze signal singularity, singular point location and size of the singular degree, fault characteristic signal can be effectively separated, and it can be used to make fault analysis and diagnosis. And choosing different wavelet analysis on fault make different results. Based on the induction motor stator current for the simulation model, the paper discusses the signal with different wavelet denoising, except the singularity detection and fault diagnosis. And analyzes the importance of fault mother wavelet choicing.

Method of finding singularities of media signal for indentification the device of its formation

Method of finding singularities of media signal for indentification the device of its formation

Improved EEMD in the Application of Signal Singularity Detection

In order to solve the endpoint effect and modal aliasing phenomenon in EMD and EEMD,Improved EEMD is put forward, and the application in signal singularity detection is researched in this paper. The improved EEMD will signal drops down into a series of different IMF to highlight the different local characteristics of original data, and then calculate Hilbert marginal spectrum and time-frequency spectrum to determine the frequency of these mutations and mutations position. To compared with FT, STFT, WVD,WT, EMD and EEMD etc, No cross-terms and no false IMF components are produced in the Hilbert time-frequency spectrum of the improved EEMD. Different frequency components and frequency mutations position are clearly distinguished at the same time. The Hilbert time-frequency spectrum of the improved EEMD has more superior detection signal singularity ability.

Algorithm Analysis on Measuring the Begin-end Time of Voltage Sag Based on Biorthogonal Wavelet

With the development of economy, technology and society, the requirements of power quality improved gradually. To improve power quality should obtain power informations first, and the direct approach of obtaining informations is detecting the power quality. It is quite effective to detect and analyze the singularity and singularity positions of signals by using wavelet transform, because wavelet transform has space localized properties. This paper firstly expounds the principle of wavelet singularity detection and the relationships between signal singularity and maximal modulus of wavelet transform. Secondly, the algorithm on measuring the begin-end time of voltage sag in the electrical distribution system is analyzed by using binary wavelet. Finally, this algorithm is simulated by MATLAB. Experimental results show that this method can detect the begin-end time of disturbance signal accurately

The Method of Precise Timing of Grounding Fault in Power System Based on Wavelet Theory

The probability of single phase grounding is very high among the faults in distribution network of our country, according to statistics it has reaching at 80 percents. Therefore, the fault selecting line of low current grounding system becomes a thorny problem, it is very often that we can’t accurately locate the fault happening time, thus couldn’t choose effective information of fault happening time. This paper puts forward the method which uses the advantage that signal singularity detection of wavelet transform of presicing timing for single phase grounding in distribution network, to pick-up detailed transient information when single phase grounding fault happening in distribution network, then the relay protection device takes action timely and accurately. Because the current signal is smaller when low current grounding happens in distribution network,it is very easier to be interfered by other devices outside, so , time orientation by singularity of zero sequence voltage signal at the fault happening time is adopted in this paper.

Fast Detection of Weak Singularities in a Chaotic Signal Using Lorenz System and the Bisection Algorithm

Signals with weak singularities are important for condition monitoring, fault forecasting, and medicine diagnosis. However, the weak singularity in a signal is usually hidden by strong noise. A novel fast method is proposed for detecting a weak singularity in a noised signal by determining a critical threshold towards chaos for the Lorenz system. First, a rough critical threshold value is calculated by local Lyapunov exponents with a step size 0.1. Second, the exact threshold value is calculated by the bisection algorithm. The advantage of the method will not only reduce the computation costs, but also show the weak singular signal which can be accurately identified from strong noise. When the variance of an external signal method embeds into a Lorenz system, according to the parametric equivalent relation between the Lorenz system and the original system, the critical threshold value of the parameter in a Lorenz system is determined.

Rapid eye movements during sleep in mice: High trait-like stability qualifies rapid eye movement density for characterization of phenotypic variation in sleep patterns of rodents

BackgroundIn humans, rapid eye movements (REM) density during REM sleep plays a prominent role in psychiatric diseases. Especially in depression, an increased REM density is a vulnerability marker for depression. In clinical practice and research measurement of REM density is highly standardized. In basic animal research, almost no tools are available to obtain and systematically evaluate eye movement data, although, this would create increased comparability between human and animal sleep studies.MethodsWe obtained standardized electroencephalographic (EEG), electromyographic (EMG) and electrooculographic (EOG) signals from freely behaving mice. EOG electrodes were bilaterally and chronically implanted with placement of the electrodes directly between the musculus rectus superior and musculus rectus lateralis. After recovery, EEG, EMG and EOG signals were obtained for four days. Subsequent to the implantation process, we developed and validated an Eye Movement scoring in Mice Algorithm (EMMA) to detect REM as singularities of the EOG signal, based on wavelet methodology.ResultsThe distribution of wakefulness, non-REM (NREM) sleep and rapid eye movement (REM) sleep was typical of nocturnal rodents with small amounts of wakefulness and large amounts of NREM sleep during the light period and reversed proportions during the dark period. REM sleep was distributed correspondingly. REM density was significantly higher during REM sleep than NREM sleep. REM bursts were detected more often at the end of the dark period than the beginning of the light period. During REM sleep REM density showed an ultradian course, and during NREM sleep REM density peaked at the beginning of the dark period. Concerning individual eye movements, REM duration was longer and amplitude was lower during REM sleep than NREM sleep. The majority of single REM and REM bursts were associated with micro-arousals during NREM sleep, but not during REM sleep.ConclusionsSleep-stage specific distributions of REM in mice correspond to human REM density during sleep. REM density, now also assessable in animal models through our approach, is increased in humans after acute stress, during PTSD and in depression. This relationship can now be exploited to match animal models more closely to clinical situations, especially in animal models of depression.

Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform

The rolling element bearing characteristic frequencies contain very little energy and are usually overwhelmed by noise and higher level of structural vibrations. The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This makes it very suitable for the detection of singularity generated by localized defects in a mechanical system. However, most applications of the continuous wavelet transform have widely focused on the use of the Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real, which the Fourier spectrum has no complex phase and the Hermitian wavelet does not affect the phase of a signal in complex domain. This gives the desirable ability to detect the singularity characteristic of a signal precisely. In this study, the Hermitian wavelet amplitude and phase map are used in conjunction to detect and diagnose the bearing fault. The Hermitian wavelet amplitude and phase map are found to show distinctive signatures in the presence of bearing inner race or outer race damage. The simulative and experimental results show that the Hermitian wavelet amplitude and phase map can extract the transients from strong noise signals and can effectively diagnose bearing faults.

The central signal singularity phenomenon in balanced SSFP and its application to positive-contrast imaging

Small perturbations of steady-state sequence parameters can induce very large spectral profile deviations that are localized to specific off-resonant frequencies, denoted critical frequencies. Although, a small number of studies have previously considered the use of these highly specific modulations for MR angiography and elastography, many potential applications still remain to be explored. An analysis of this phenomenon using a linear systems technique and a geometric magnetization trajectory technique shows that the critical frequencies correspond to singularities in the steady-state signal equation. An interleaved acquisition combined with a complex difference technique yields a spectral profile containing sharp peaks interleaved with wide stopbands, while a complex sum technique yields a spectral profile similar to that of balanced steady-state free precession. Simulations and phantom experiments are used to demonstrate a novel application of this technique for positive-contrast imaging of superparamagnetic iron-oxide nanoparticles. The technique is shown to yield images with high levels of positive contrast and good water and fat background suppression. The technique can also simultaneously yield images with contrast similar to balanced steady-state free precession.

Development of General Embedded Intelligent Monitoring System for Tower Crane

Development of General Embedded Intelligent Monitoring System for Tower Crane

Automatic validation of vibration signals in wind farm distributed monitoring systems

A rapid growth of wind farms as a leading renewable energy sector has compelled a number of companies to develop dedicated distributed systems of monitoring and diagnosis (SM&D). Such systems are capable of early mechanical faults detection, which prevents from costly critical repairs. Fault detection of wind turbines is based on vibration and process signals analysis. Modern SM&D are usually advanced hardware and software technological products, which on the basis of collected data are capable of continuous execution of enormous number of analyses. However, the expansion of distributed systems has disclosed new challenges accompanying the transition from a singular signal analysis to automatic interpretation of large data sets. The paper deals with the assessment of data acquisition final products, namely the correctness of vibration signals recording process. The proposed validation, which is to be implemented a priori to data analysis, is required for significant calculation of any signal features as well as for legitimate storage of raw waveforms in the system database. The latter aspect is especially important in terms of gigabytes of disk space frequently wasted for corrupted data. The paper presents a step-by-step method for automatic signal validation, which can be implemented on a personal computer. Finally, the algorithm is evaluated on a real wind turbine database.

Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.

Single-Phase-to-Earth Fault Detection for High Voltage Transmission Line Based on Wavelet Analysis

The voltage and current signals are transient signals with singularity and mutability when single-phase-to-earth fault occurs in high-voltage transmission line, of which the singular point and mutations parts contain rich fault information. However, wavelet analysis just has the nature of singularity detection and zoom properties, which is very effective in singularity analysis and singular points location. Based on the basic principle of wavelet analysis and signal singularity detection, simulation and detection of singularity of fault voltage and current signals in high voltage transmission line are made by db5 wavelet multi-scale comprehensive analysis method. The singularity detection and calculation program are accomplished in M-file form by programming in MATLAB, the simulation results show that db5 wavelet can detect the singularity of fault signal and determine the fault occurrence and end time fast and accurately. This method enhanced accuracy and reliability of single-phase-to-earth fault detection.

Detection of small leakage from long transportation pipeline with complex noise

In the long distance pipeline remote monitoring system, small leak detection becomes an important issue. Weak singularities in small leak signals are usually difficult to detect precisely under complicated noise background, which may cause false alarm or miss alarm. The advantage of applying the harmonic wavelet method is explored in this paper. Pipeline small leak sensitive characteristics are recognized and the negative pressure wave inflexions are extracted by harmonic wavelet analysis, expressed in terms of harmonic wavelet time-frequency mesh map, time-frequency contour map, and time-frequency profile plot. This paper also presents a comparative study of both Daubechies wavelet and harmonic wavelet analysis when applied to pipeline small leak detection under complicated background noises. Results of simulating test and field experiment show that it is possible to distinguish weak non-stationarities from complicated noises by harmonic wavelet analysis in pipeline small leak detection system. The comparison clearly illustrates that harmonic wavelet based pipeline small leakage detection method is significantly more accurate than other wavelets analysis such as Daubechies wavelet. This work provides a reliable and safe guarantee for oil and gas long distance transportation, reducing petroleum product losses and protecting surrounding environment.

Detection Mechanism of Fuzzy Singular Signal

摘要: 工程实际中一般很难有理想的奇异性信号,它们的奇异性往往表现得很模糊。该文从理论上分析了这种模糊奇异性信号的小波检测机理,证明了小波对模糊奇异性信号的检测相当于用一个新小波对理想奇异性信号进行检测,给出了新小波的生成定义与实例、理想奇异性信号的模糊化模拟与检测实例,也给出了一个工程实际模糊奇异性信号的奇异性检测过程,这些实例结果证明了理论分析的正确性。 关键词: 信号处理; 模糊化奇异性; 奇异性检测; 小波变换

Black hole phase transitions in Hořava-Lifshitz gravity

We study black hole phase transitions in (deformed) Ho\v{r}ava-Lifshitz (H-L) gravity, including the charged black hole and KS black hole. Stability of black holes in H-L gravity and the phase transition signals are investigated. The Ricci scalar of the thermodynamical metrics of black holes in H-L gravity are calculated for the purpose of verifying or falsifying the correspondence between curvature singularities of thermodynamical metrics and phase transition signals proposed in [arXiv:1008.4482]. We find a probable counter-example to the correspondence that there is an infinite discontinuity at the specific heat curve for charged black hole with hyperbolic event horizon in H-L gravity but without a corresponding curvature singularity of thermodynamical metrics. The violation of the correspondence in H-L gravity may due to the non-spherical topology of its event horizon. We guess that the topology of the event horizon and the dimension of spacetime may influence the validity of the correspondence. Our results may provide new insights towards a better understanding of the H-L gravity, as well as black hole thermodynamics.

A Practical Approach to Detection and Analysis of Time-Varying Waveform

The power quality has attracted considerable attention from both utilities and users due to the development of sensitive electronic equipment in power system network. A novel method for detecting and localizing power quality disturbance based on wavelet neural network is presented. The wavelet transform is established on decomposition of a signal according to time-scale using basis functions with adaptable scaling properties known as multiresolution analysis. The wavelet transform expands a signal not in terms of a trigonometric polynomial but by wavelets, generated using transition and dilation of a fixed wavelet function. The feature obtained from wavelet transform coefficients are inputted into the neural network for power quality disturbance pattern recognition. The improved network algorithm is utilized to complete the network structure initialization. The fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component. The simulation results show that the proposed method is more sensitive to signal singularity, and achieves better performance over traditional method.

Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance

Accurate detection of heavy metal-induced stress on the growth of crops is essential for agricultural ecological environment and food security. This study focuses on exploring singularity parameters as indicators for a crop's Zn stress level assessment by applying wavelet analysis to the hyperspectral reflectance. The field in which the experiment was conducted is located in the Changchun City, Jilin Province, China. The hyperspectral and biochemistry data from four crops growing in Zn contaminated soils: rice, maize, soybean and cabbage were collected. We performed a wavelet transform to the hyperspectral reflectance (350–1300 nm), and explored three categories of singularity parameters as indicators of crop Zn stress, including singularity range ( S R ), singularity amplitude ( S A ) and a Lipschitz exponent ( α). The results indicated that (i) the wavelet coefficient of the fifth decomposition level by applying Daubechies 5 (db5) mother wavelets proved successful for identifying crop Zn stress; the S R of crop concentrated on the region was around 550–850 nm of the spectral signal under Zn stress; (ii) the S R stabilized, but S A and α had developed some variations at the growth stages of the crop; (iii) the S R , S A and α were found among four crop species differentially; and moreover the S A increased in relation to an increase in the S R of crop species; (iv) the α had a strong non-linear relationship with the Zn concentration ( R 2:0.7601–0.9451); the S A had a strong linear relationship with Zn concentration ( R 2:0.5141–0.8281). Singularity parameters can be used as indicators for a crop's Zn stress level as well as offer a quantitative analysis of the singularity of spectrum signal. The wavelet transform technique has been shown to be very promising in detecting crops with heavy metal stress.

Analysis on Singularity of Fault Signals of High Spindle Based on Hermitian Wavelet

The signal in the nature ofsingularity is always caused by mechanical fault of CNC machine tool.It is important to recognize the singularity correctly for mechanical fault diagnosis. This paper deals with the wavelet t ransform and the relation between the modulus maxima and the singularity detection , and based on the excellent property of the Hermitian wavelet , poses the conception that applies it in fault diagnosis of the high speed spindle. On the base of analyzing singularity characters and wavelet detection mechanism of the singular signals ,the Hermitian continuous wavelet transform is applied in extraction and analysis of the fault signals.Fault detection and positioning of high speed spindle is completed by the modulus maxima point distribution in transform.Results of simulation verify the effectiveness of the method.