scholarly journals Time-Frequency Analysis of Compressor Rotating Stall by Means of Wavelet Transform

1996 ◽  
Author(s):  
Shengfang Liao ◽  
Jingyi Chen
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Rotating Stall ◽  
Processing Methods ◽  
Stall Inception ◽  
Time Frequency ◽  
Signal Processing Methods ◽  
Conventional Signal

In this paper, an application of Wavelet Transform, which is a newly developed time-frequency technique of signal processing, is demonstrated in analyzing compressor rotating stall signals. In contrast to conventional signal processing methods, e.g. Fourier Transform, Wavelet Transform is very suitable for analyzing transient processes as rotating stall inception in compressors. In this study, some typical rotating stall signals are processed via Morlet’s wavelet. It is concluded that Wavelet Transform has a great advantage in detecting rotating stall inceptions, which are usually very weak and embedded in relatively stronger noises. In the diagrams resulted from the transform, every emergence of precursor as well as full stall signals of a certain frequency is illustrated versus time.

Phase-based spectrum analysis method for identifying weak harmonics

2018 ◽  
Vol 24 (23) ◽  
pp. 5585-5596 ◽  
Author(s):  
Jingsong Xie ◽  
Wei Cheng ◽  
Yanyang Zi ◽  
Mingquan Zhang
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Discrete Wavelet ◽  
Accurate Identification ◽  
Processing Methods ◽  
Characteristic Frequencies ◽  
Frequency Detection ◽  
Signal Processing Methods

Fault characteristic frequency extraction is an important means for the fault diagnosis of rotating machineries. Traditional signal processing methods commonly use the amplitude information of signals to detect damages. However, when the amplitudes of characteristic frequencies are weak, the recognition effects of traditional methods may be unsatisfactory. Therefore, this paper proposes the phase-based enhanced phase waterfall plot (EPWP) method and frequency equal ratio line (FERL) method for identifying weak harmonics. Taking a cracked rotor as an example, the characteristic frequency detection performances of the EPWP and FERL methods are compared with that of the traditional signal processing methods namely fast Fourier transform, short-time Fourier transform, discrete wavelet transform, continuous wavelet transform, ensemble empirical mode decomposition, and Hilbert–Huang transform. Research results demonstrate that the effects of EPWP and FERL for the recognitions of weak harmonics which are contained in steady signals and transient signals are better than that of the traditional signal processing methods. The accurate identification of weak characteristic frequencies in the vibration signals can provide an important reference for damage detections and improve the diagnostic accuracy.

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

Application of Advanced Signal Processing Methods to Automotive Systems Testing

2004 ◽  
Author(s):  
Steve M. Rohde ◽  
William J. Williams ◽  
Mitchell M. Rohde
Keyword(s):  
Signal Processing ◽  
Consumer Preferences ◽  
Processing Methods ◽  
Time Frequency ◽  
Vehicle Systems ◽  
Pedal Feel ◽  
Mathematical Analyses ◽  
Noise Vibration ◽  
Virtual Product Development ◽  
Signal Processing Methods

During the past twenty years there have been rapid developments in the creation and application of mathematical computer-based capabilities and tools (e.g., FEA) to simulate and synthesize vehicle systems. This has led to the concept of virtual product development. In parallel with the development of these tools, an equally sophisticated set of tools have been developed in the area of advanced signal processing. These tools, based upon mathematical and statistical modeling techniques, enable the extraction of useful information from data and have application throughout the entire vehicle creation process. Moreover, signal processing bridges the gap between the “virtual” and the “real” worlds — an extremely important concept that is changing the entire nature of what is thought of as “testing.” This paper discusses the use of advanced signal processing methods in vehicle creation with particular emphasis on its use in vehicle systems testing. Modern Time Frequency Analysis (TFA), a technique that was specifically designed to study transient signals and was in part pioneered by one of the authors (WJW), is highlighted. TFA expresses a signal jointly in time and frequency at very high resolution and as such can often provide profound insights. Applications of TFA to vehicle systems testing are presented related to Noise, Vibration, and Harshness (NVH) that enable sound quality analyses. For example, using TFA predictive models of consumer preferences for transient sounds that are useful to the automotive engineer in testing and modifying new vehicle subsystem designs are discussed. Other applications that are discussed deal with brake pedal feel, and characterizing vehicle crash signals. In the latter case TFA has resulted in some unique insights that were not provided by conventional statistical and mathematical analyses.

Comparative characteristics of anti-collision processing of radio signal from identification tags on surface acoustic waves

2019 ◽  
pp. 48-56 ◽  
Author(s):  
A. V. Sorokin ◽  
A. P. Shepeta ◽  
V. A. Nenashev ◽  
G. M. Wattimena
Keyword(s):  
Signal Processing ◽  
Surface Acoustic Waves ◽  
Group Identification ◽  
Sensor Data ◽  
Correct Identification ◽  
Processing Methods ◽  
Time Frequency ◽  
Advantages And Disadvantages ◽  
Identification Tags ◽  
Signal Processing Methods

Introduction:Collision of information signals is a common problem in the measurement of physical magnitudes, such as temperature, pressure, stress, etc., with acoustic-electronic sensors. This problem is caused by overlapping response signals in the time domain, which makes it difficult to interpret correctly the device identification codes or the sensor data received.Purpose:Analysis of anticollision algorithms for radio-frequency tag code detection and identification by response information signals from acoustic-electronic devices which use the methods of time, frequency and frequency-time division of the response radio signals.Methods:Probabilistic methods for calculating the parameters of digital detectors of radio pulse bursts with given false alarm values and gaussian white noise background; individual code group identification methods when studying the attenuation of acoustic-electric signal during their propagation in the tag substrate, taking into account the dependence of the attenuation on the tag topology.Results:We have derived analytical expressions to calculate the probability of the correct identification of each tag, taking into account the dependence on tag topology, attenuation characteristics, the anti-collision signal processing methods and the signal-to-noise ratios. Curves which allow you to compare the advantages and disadvantages of the considered anti-collision signal processing methods are calculated and shown in the article. The analysis of the graphic charts demonstrating the correct identification probability has shown that identification tags with frequency-time coding have better ratios as compared to frequency or time methods of collision prevention.Practical relevance:The obtained result allows you to effectively evaluate the condition of technical objects, improving the predictability and prevention of possible environmental and man-made disasters.

scholarly journals Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods

2021 ◽  
Author(s):  
Ebru Sayilgan ◽  
Yilmaz Kemal Yuce ◽  
Yalcin Isler
Keyword(s):  
Signal Processing ◽  
Steady State ◽  
Evoked Potentials ◽  
Wavelet Function ◽  
Processing Methods ◽  
Training Time ◽  
Time Frequency ◽  
Wavelet Features ◽  
Classification Evaluation ◽  
Signal Processing Methods

Steady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and are in use in many areas such as clinical neuroscience, cognitive science, and engineering. SSVEPs have become popular recently, due to their advantages including high bit rate, simple system structure and short training time. To design SSVEP-based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. In this study, we investigated both the effect of choosing a mother wavelet function and the most successful combination of classifier algorithm, wavelet features, and frequency pairs assigned to BCI commands. SSVEP signals that were recorded at seven different stimulus frequencies (6–6.5 – 7 – 7.5 – 8.2 – 9.3 – 10 Hz) were used in this study. A total of 115 features were extracted from time, frequency, and time-frequency domains. These features were classified by a total of seven different classification processes. Classification evaluation was presented with the 5-fold cross-validation method and accuracy values. According to the results, (I) the most successful wavelet function was Haar wavelet, (II) the most successful classifier was Ensemble Learning, (III) using the feature vector consisting of energy, entropy, and variance features yielded higher accuracy than using one of these features alone, and (IV) the highest performances were obtained in the frequency pairs with “6–10”, “6.5–10”, “7–10”, and “7.5–10” Hz.

scholarly journals Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

2017 ◽  
Vol 3 (3) ◽  
pp. 146
Author(s):  
Yovinia Carmeneja Hoar Siki ◽  
Natalia Magdalena Rafu Mamulak
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Music Signal ◽  
Short Time

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

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