Observing sugar-beet quality using process and signal-analysis methods

1999 ◽  
Vol 7 (7) ◽  
pp. 881-890 ◽  
Author(s):  
A. Arenz ◽  
M. Reimann ◽  
E. Schnieder ◽  
U. Harten
Keyword(s):  
Sugar Beet ◽  
Signal Analysis ◽  
Analysis Methods

scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Nonlinear Signal Analysis Methods in the Elucidation of Protein Sequence−Structure Relationships

2002 ◽  
Vol 102 (5) ◽  
pp. 1471-1492 ◽  
Author(s):  
Alessandro Giuliani ◽  
Romualdo Benigni ◽  
Joseph P. Zbilut ◽  
Charles L. Webber, ◽  
Paolo Sirabella ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Signal Analysis ◽  
Sequence Structure ◽  
Analysis Methods ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

scholarly journals Recent Advances in Brain Signal Analysis: Methods and Applications 2018

2018 ◽  
Vol 2018 ◽  
pp. 1-2 ◽  
Author(s):  
Victor Hugo C. de Albuquerque ◽  
Plácido Rogerio Pinheiro ◽  
Roshan J. Martis ◽  
João Manuel R. S. Tavares
Keyword(s):  
Signal Analysis ◽  
Analysis Methods ◽  
Recent Advances

scholarly journals Assessing Diagnostic Techniques for Poblem Identification in Advanced Industrial Gas Turbines

1990 ◽  
Author(s):  
Alexander Lifson ◽  
Anthony J. Smalley ◽  
George H. Quentin ◽  
Joseph P. Zanyk
Keyword(s):  
Power Generation ◽  
Electrical Power Generation ◽  
Gas Turbines ◽  
Signal Analysis ◽  
Electrical Power ◽  
Diagnostic Techniques ◽  
Operating Conditions ◽  
Recent Past ◽  
Analysis Methods ◽  
Industrial Gas Turbines

This paper describes existing, developing, and needed methods for detection, identification, and diagnosis of problems in combustion turbines. The use of combustion turbines for electrical power generation is growing, and advanced models of large industrial turbines are now starting to enter service. In view of the harsh operating conditions and severe service to which these new turbines will be exposed, this paper evaluates sensors and signal analysis methods to detect and diagnose the problems which may surface in operation. Generic problems which have been observed in combustion turbine installations in the recent past are identified, and methods for detecting these problems, quantifying them, and isolating their causes are analyzed.

Non Standard Signal Analysis Methods for Failure Detection

1995 ◽  
Vol 28 (19) ◽  
pp. 321-327
Author(s):  
L.V. Pérez ◽  
M.C. Parpaglione ◽  
D. Czibener ◽  
M.E. Pepe ◽  
C.E. D’Attellis ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Failure Detection ◽  
Standard Signal ◽  
Analysis Methods
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