scholarly journals Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals.

2020 ◽  
Author(s):  
Miguel Enrique Iglesias Martínez
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Statistical Signal Processing ◽  
Features Extraction ◽  
Electrical Machines

scholarly journals Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽  
2018 ◽  
Vol 6 (7) ◽  
pp. 124 ◽  
Author(s):  
Elena Barton ◽  
Basad Al-Sarray ◽  
Stéphane Chrétien ◽  
Kavya Jagan
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Data Analysis ◽  
Real Data ◽  
Real Data Analysis ◽  
Convex Optimisation ◽  
Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

FAULT DIAGNOSIS OF MULTITOOTH MACHINE TOOL BASED ON STATISTICAL SIGNAL PROCESSING

2002 ◽  
Vol 35 (1) ◽  
pp. 197-202 ◽  
Author(s):  
Aníbal Reñones Domínguez ◽  
Luis J. De Miguel González
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Machine Tool ◽  
Statistical Signal Processing

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.

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