A comparison of time‐frequency signal processing methods for identifying non‐perennial streamflow events from streambed surface temperature time series

2021 ◽  
Author(s):  
D. Partington ◽  
M. Shanafield ◽  
C. Turnadge
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Surface Temperature ◽  
Temperature Time Series ◽  
Frequency Signal ◽  
Processing Methods ◽  
Time Frequency ◽  
Signal Processing Methods ◽  
Temperature Time

Analysis of Stall Precursors and Determination of Optimum Signal Processing Methods for an LM-2500 Gas Turbine

2008 ◽  
Author(s):  
Knox T. Millsaps ◽  
Gustave C. Dahl ◽  
Daniel E. Caguiat ◽  
Jeffrey S. Patterson
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Gas Turbine ◽  
Rotor Speed ◽  
Processing Methods ◽  
Turbine Engine ◽  
Pressure Signal ◽  
Time Series Analyses ◽  
Signal Processing Methods

This paper presents an analysis of data taken from several stall initiation events on a GE LM-2500 gas turbine engine. Specifically, the time series of three separate pressure signals located at compressor stages 3, 6, and 15 were analyzed utilizing various signal processing methods to determine the most reliable indicator of incipient stall for this engine. The spectral analyses performed showed that rotating precursor waves traveling around the annulus at approximately half of the rotor speed were the best indicators. Non-linear chaotic time series analyses were also used to predict stall, but it was not as reliable an indicator. Several algorithms were used and it was determined that stall wave perturbations can be reliably identified about 900 revolutions prior to the stall. This work indicates that a single pressure signal located at stage 3 on an LM-2500 gas turbine is sufficient to provide advance warning of more than 2 seconds prior to the fully developed stall event.

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.

Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur

2017 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
Hutomo Atman Maulana ◽  
Muliah Muliah ◽  
Maria Zefaya Sampe ◽  
Farrah Hanifah
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sample Size ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Sea Surface ◽  
Series Data ◽  
Forecast Time ◽  
Temperature Time

The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with  MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method

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