scholarly journals A Comparative Study of Signal Processing Techniques for Monitoring and Diagnostics

1997 ◽  
Author(s):  
F. A. Andrade ◽  
I. I. Esat
Keyword(s):  
Signal Processing ◽  
Volterra Series ◽  
Industrial Condition ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Orthogonal Wavelets ◽  
Multilayer Perceptron Network ◽  
Monitoring And Diagnostics ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper presents a brief review of the signal processing techniques being used in industrial condition monitoring and diagnostics. Four main types of monitoring methods are discussed; Neural Networks, Wavelets, Time-Frequency methods, and Non-linear series. References to existing applications are also included throughout the paper. This aims to introduce the most common techniques which are being used today, namely the multilayer perceptron network, orthogonal and non-orthogonal wavelets, spectrogram, Wigner and Choi-Williams distributions, and Volterra series.

scholarly journals Cutting-edge Mathematical Tools in Processing and Analysis of Signals in Marine and Navy

2012 ◽  
Vol 1 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Igor Vujović ◽  
Joško Šoda ◽  
Ivica Kuzmanić
Keyword(s):  
Decision Making ◽  
Signal Processing ◽  
Fourier Transform ◽  
Video Stream ◽  
Information Gathering ◽  
Pivotal Role ◽  
Surveillance Video ◽  
Time Frequency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Signal processing plays a pivotal role in information gathering and decision making. This paper presents and compares different signal processing techniques used in marine and navy applications, primarily based on using wavelets as kernel. The article covers Fourier transform, time frequency wavelet based techniques such as bandelets, contourlets, curvelets, edgelets, wedgelets, shapelets, and ridgelets. In the example section of the paper, several transform techniques are presented and commented on the harbour surveillance video stream example.

scholarly journals A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3037
Author(s):  
Miguel Luján ◽  
María Jimeno ◽  
Jorge Mateo Sotos ◽  
Jorge Ricarte ◽  
Alejandro Borja
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Autobiographical Memory ◽  
Memory Deficits ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Time Frequency ◽  
Machine Learning Classification ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Recent Advances in Vibration Signal Processing Techniques for Gear Fault Detection-A Review

2013 ◽  
Vol 430 ◽  
pp. 78-83 ◽  
Author(s):  
Rusmir Bajrić ◽  
Ninoslav Zuber ◽  
Safet Isić
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Vibration Signal ◽  
Analysis Time ◽  
Time Frequency ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Gear Fault ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and nonstationary operation. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review.

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