Intensity analysis methods for transient signals

Transient dynamical analysis of strain signals in sheet metal stamping processes

This paper proposes a method of extracting transient signals in the presence of colored noise using an adaptive line enhancer. Transient signals are non-stationary signals with a shorter duration than the observation interval. The problem of transient signal extraction arises in many fields of application - for instance, in mechanical failure diagnostics and in underwater acoustic warfare - where the transient signals usually contain a wealth of useful object feature information. But it is very difficult to extract transient signals with only a few oscillation cycles in a strongly colored noise background. The traditional method of improving the signal to noise ratio is not very effective. Some people try to use the Hilbert-Huang Transform method, but the method itself has many problems, such as the lack of rigorous physical meaning, and probable existence of serious waveform distortion. We have trialled the use an adaptive line enhancer (ALE) to extract the transient signals with encouraging results. Here an adaptive line enhancer is a degenerate form of the adaptive noise canceller in that its reference signal consists of a delayed version of the input signal. According to the change of the input signal, the adaptive filter coefficients are updated constantly by using a recursive structure. An adaptive line enhancer can separate the input signal into a strong correlation component as the filtered output and produce a weak correlation component as an estimation error. Transient signals will emerge in the error component when the adaptive algorithm convergence speed is slow enough. If the interference noise is colored noise sharing the larger proportion in the observed signal, the above methods can achieve high processing gain with little waveform distortion even if the frequency of the strong interfering signal and transient signals are the same. Here, we will describe the complete process that separates transient signals from the colored noise by using ALE. Finally, computer simulation results are presented. The simulation results indicate that: when the step parameter value is small enough, the transient signal can be extracted perfectly from the strong noise background; but with the increase of the step parameter value, the waveform distortion will increase seriously.

Linear and quadratic time‐frequency transforms are used to investigate and characterize transient signals from several wave propagation problems in structural acoustics. The transforms include: the short‐time spectrum, Wigner type representations, and wavelet methods. Advantages and disadvantages of the various representations within the present context are presented. Particular emphasis is placed on using the wavelet packet method of Coifman to analyze and characterize complex transient signals from various structural‐acoustic transmission paths with dispersive characteristics. Adaptive procedures are used to determine optimal time‐frequency localization by using the best basis functions for the class of dispersive signals of interest. Various features and parameter extraction methods based on the local maxima of the wavelet transform at each scale are presented for several complex transient dispersive signals. [Work supported by ONR.]

The Wigner-Ville distribution (WVD) is a widely used tool in the time-frequency analysis of non-stationary signals. However, the presence of false-terms in WVD for multicomponent signals can limit its applicability and interpretation. Various kernel and window-based smoothing methods have been used to remove false-terms from WVD, but they often come at the cost of reduced time-frequency resolution of autoterms. This paper proposes a novel sliding time and frequency windowing-based technique for removing false-terms from WVD, which aims to overcome the limitations of kernel-based methods. The proposed method segments a multi-component signal using overlapping windows in time and frequency domains successively and the WVD of each windowed signal is computed. The WVDs of all windowed signals are added together to obtain the falseterm free WVD. Energy scaling is also applied to minimize the effect of overlapping windows. Performance of the proposed method is evaluated for different multi-component synthetic signals and a natural ECG signal using various performance measures. The simulation results demonstrate that the proposed method can effectively remove false-terms from the WVD with improved auto-term enhancement and time-frequency resolution. Results from the proposed method are also compared with different kernel and window-based smoothing methods to show its superiority over these methods.

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research task using guided acoustic signals. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures are generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The report presents the background the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. The report presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The results of this research show the feasibility of on-line monitoring of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite.

Children with cancer must cope not only with their disease and its treatment but also with the environment in which treatment is given. The intensities of sound and light levels required to perform necessary patient care may result in a disruptive nighttime care environment. The purpose of this study was to describe nighttime patterns of environmental factors, sound, light, and temperature levels, at the bedside of children with cancer receiving inpatient chemotherapy. Participants were 15 school-aged children receiving chemotherapy on an inpatient pediatric oncology unit. Sound, light, and temperature in the child's room were measured continuously using a digital-sound pressure-level meter and an external channel data logger. Mean nighttime sound levels were 49.5 (SD, 3.1) dB (range, 34.6-84.8 dB). Sound and light intensities were greatest early in the shift and decreased through the night. A basic mixed linear model identified significant main effects of time of night for both sound (F=50.42, P<.01) and light (F=12.43, P<.01). Study findings identified a bedside care environment with persistently elevated sound levels and abrupt increases in sound intensity throughout the night. Such a disruptive nighttime environment is not conducive to restful nighttime sleep and may serve as an additional source of physiological and psychological stress for hospitalized children with cancer. Efforts are needed to identify modifiable sources of nighttime sound and develop interventions to reduce nighttime sound. Collaborative efforts to organize clinical care to minimize nighttime disruptions may lead to reduced bedside sound levels.

Measurement and analysis of sound levels from a RASS site near Barrow, Alaska

Time&#x2013;frequency analysis (TFA) methods can characterize the instantaneous frequency (IF) features of nonstationary signals by constructing the time-domain signal model. However, the traditional TFA methods are ineffective for transient signals that are discontinuous in the time domain and characterized by sharp frequency variation. Therefore, this article proposes a new TFA method called generalized transient-squeezing transform (GTST) to process transient signals by constructing a signal model in the frequency domain. First, the generalized group delay (GD) estimation is calculated based on the frequency-domain signal model. Then, the generalized transient-squeezing operator is constructed by iterative operations. Finally, the time&#x2013;frequency (TF) energy is rearranged according to the constructed operator. The GTST provides a more compact TF representation (TFR) of the transient signal while maintaining reversibility. Numerical validation shows that the proposed method has higher TF concentration and accuracy in analyzing transient signals compared with other advanced TFA methods. Experimental validation shows the effectiveness of the proposed method in practical transient signal analysis.

According to the non-stationary of PD pulse signal, the Joint Time-Frequency Analysis (JTFA) is put forward to process the signal to obtain their characteristics in time-frequency domain. At present, the Wigner-Ville Distribution (WVD) which belongs to the Cohen class Time-Frequency Energy Distribution is popular in processing the non-stationary signals. So the simulation PD pulse is chosen as the subject investigated to study on the original WVD, Smoothing-Pseudo WVD (SPWVD) and Rearranged-Smoothing-Pseudo WVD (RSPWVD) in this paper. The results show that WVD provides a high sensitivity to variation in short duration of non-stationary PD signal, which is available to extract the time-varying information. And the SPWVD can remove the interference cross-terms with reducing the time-frequency resolution, which is caused by bilinear function. For having good time-frequency concentration, the RSPWVD provides a powerful tool to evaluate parameters for single PD pulse shape.

This paper presents the methodology, properties and processing of the time-frequency techniques for non-stationary signals, which are frequently used in biomedical, communication and image processing fields. Two classes of time-frequency analysis techniques are chosen for this study. One is short-time Fourier Transform (STFT) technique from linear time-frequency analysis and the other is the Wigner-Ville Distribution (WVD) from Quadratic time-frequency analysis technique. Algorithms for both these techniques are developed and implemented on non-stationary signals for spectrum analysis. The results of this study revealed that the WVD and its classes are most suitable for time-frequency analysis.

Non-stationary and nonlinear signals, which can bring important applications in chaotic dynamics, and are found in several scientific and engineering fields. Several processing techniques have been used to understand and extract information from these signals, and the literature shows that time-frequency analysis techniques are suitable tools for this characterization. They allow to examine the time-varying characteristics of the signals. In this chapter, we will explore time-frequency methods applied especially to nonlinear signals. First, we discuss the diverse range of dynamical systems. Then, we introduce the classical time-frequency methods, including the Short-Time Fourier Transform, the Wavelet Transform, the Hilbert Transform, and the Wigner-Ville distribution. These methods have been widely used in the literature in the study of non-stationary operations. Thus, we present emerging methods of time-frequency analysis, taking advantage of post-processing and synchrosqueezing techniques to improve the accuracy and resolution of the time-frequency representation. We present a comprehensive analysis of these emerging methods, comparing them with classical approaches to show their contributions. Our main goal is to highlight the capabilities of these emerging time-frequency analysis methods in capturing and understanding chaotic patterns in signals.

It is very useful to study the signals radiated from corona discharges for the purposes of high-voltage line monitoring. Time-frequency analysis can clearly reveal the time-varying spectrum characteristics of such signals, which is very useful for analyzing and processing the non-linear and non-stationary weak signals, such as the signals radiated from corona discharges. Several time-frequency analysis methods, such as the Short-Time Fourier Transform (STFT), Wigner-Ville distribution and the Hilbert-Huang Transform (HHT) and so on, are used in this paper. The simulation data with the same and different amplitudes are comparatively analyzed by these time-frequency distribution methods. It can be concluded that the time-frequency analysis method based on HHT is more efficient to identify and suitable for the non-linear and non-stationary weak signals.

The sound pressure level measured at a distance of one meter is frequently used to assess the sound emission of a machine. The sound pressure level depends on the measuring distance, the directivity pattern, if any, of the machine and on the sound‐absorbing properties of the place of installation. Because it is a local field parameter it is not very suitable for ascertaining the sound emission of a machine. In contrast to, the sound power level is far more suitable because the influence mentioned earlier can be corrected and the value is reproducible for any random test condition. Generally, it is calculated from the results of sound pressure level measurements by application of the standardised enveloping surface method. Concerning sound intensity measurements a special probe allows ascertainment of the sound intensities directly on the enveloping surface. Despite an initially elaborate measuring technology, this method is in fact often faster and more exact than the enveloping surface method because it does not require subsequent correction to determine the sound power level. The benefits and drawbacks of sound pressure and sound intensity measurements are compared with each other on the basis of measurements on centrifuges directly related to practical operation.

Transient analysis is carried out for the single-phase grounding fault in small current grounding system, the transient grounding current expression is derived, and the influence factors are analyzed. Introduces a method for non-stationary and non-linear signal analysis method-Hilbert Huang transform (HHT) to analyze the single phase grounding fault in small current grounding system, HHT can be better used to extract the abundant transient time frequency information from the non-stationary and nonlinear fault current signals. The empirical mode decomposition (EMD) process and the normalized Hilbert Huang transform (NHHT) algorithm are presented, NHHT is used to analyze and verify an example of the nonlinear and non-stationary amplitude modulation signals. Build a small current grounding system in the EMTP_ATP environment, by selecting the appropriate time window to extract the transient signals, NHHT is used to analyze the transient current signals, and the Hilbert amplitude spectrum and the Hilbert marginal spectrum of the zero sequence transient current signals are obtained.

It is well know that under water vehicles emit many types of acoustic signals, including stationary as well as non-stationary. The non-stationary transient signals are receiving considerable attention, especially from the standpoint of detection and tracking of under water vehicles. Thus, modern methods for processing these non-stationary acoustic transient signals are required. This paper presents a very powerful method for analyzing acoustic transient signals, the positive time-frequency distribution. A fast algorithm that implements the minimum cross-entropy positive time-frequency distribution makes practical the processing of ’’real world data‘‘, like under water vehicle acoustic transient signals. An example of such a signal is presented, which is a generic acoustic transient signal from a under water vehicle. The signal is only representative, as it is normalized in time, in frequency, and in amplitude. The positive time-frequency distribution constructed for this generic transient signal is contrasted with the one-third octave method, which is currently the primary method being used by the under water vehicle community to analyze under water vehicle transients. The positive distribution is also contrasted with broad band and narrow band spectrograms.