scholarly journals On the selection of advanced signal processing techniques for guided wave damage identification using a statistical approach

2014 ◽  
Vol 67 ◽  
pp. 50-60 ◽  
Author(s):  
Ching-Tai Ng
Keyword(s):  
Signal Processing ◽  
Statistical Approach ◽  
Damage Identification ◽  
Guided Wave ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Selection Of

scholarly journals Structural Health Monitoring of Steel Pipes under Different Boundary Conditions and Choice of Signal Processing Techniques

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Rais Ahmad ◽  
Tribikram Kundu
Keyword(s):  
Signal Processing ◽  
Boundary Conditions ◽  
Fourier Transforms ◽  
Guided Wave ◽  
Continuous Wavelet ◽  
Mother Wavelet ◽  
Different Boundary Conditions ◽  
Steel Pipes ◽  
Processing Techniques ◽  
Signal Processing Techniques

Guided wave technique is an efficient method for monitoring structural integrity by detecting and forecasting possible damages in distributed pipe networks. Efficient detection depends on appropriate selection of guided wave modes as well as signal processing techniques. Fourier analysis and wavelet analysis are two popular signal processing techniques that provide a flexible set of tools for solving various fundamental problems in science and engineering. In this paper, effective ways of using Fourier and Wavelet analyses on guided wave signals for detecting defects in steel pipes are discussed for different boundary conditions. This research investigates the effectiveness of Fourier transforms and Wavelet analysis in detecting defects in steel pipes. Cylindrical Guided waves are generated by piezo-electric transducers and propagated through the pipe wall boundaries in a pitch-catch system. Fourier transforms of received signals give information regarding the propagating guided wave modes which helps in detecting defects by selecting appropriate modes that are affected by the presence of defects. Continuous wavelet coefficients are found to be sensitive to defects. Several types of mother wavelet functions such as Daubechies, Symlet, and Meyer have been used for the continuous wavelet transform to investigate the most suitable wavelet function for defect detection. This research also investigates the effect of different boundary conditions on wavelet transforms for different mother wavelet functions.

scholarly journals FINANCIAL SIGNAL PROCESSING: A SELF CALIBRATING MODEL

2001 ◽  
Vol 04 (04) ◽  
pp. 567-584 ◽  
Author(s):  
ROBERT J. ELLIOTT ◽  
WILLIAM C. HUNTER ◽  
BARBARA M. JAMIESON
Keyword(s):  
Signal Processing ◽  
Markov Chain ◽  
Term Structure ◽  
Markov Models ◽  
Estimation Procedure ◽  
Rate Process ◽  
Treasury Bills ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Selection Of

Previous work on multifactor term structure models has proposed that the short rate process is a function of some unobserved diffusion process. We consider a model in which the short rate process is a function of a Markov chain which represents the "state of the world". This enables us to obtain explicit expressions for the prices of zero-coupon bonds and other securities. Discretizing our model allows the use of signal processing techniques from Hidden Markov Models. This means we can estimate not only the unobserved Markov chain but also the parameters of the model, so the model is self-calibrating. The estimation procedure is tested on a selection of U.S. Treasury bills and bonds.

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Gert Van Dijck ◽  
Marc M. Van Hulle
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Cell Types ◽  
Signal Quality ◽  
Quality Signals ◽  
Processing Techniques ◽  
Electrode Selection ◽  
Signal Processing Techniques ◽  
Processing Steps ◽  
Selection Of

AbstractRecently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”

scholarly journals Structural Damage Location by Low-Cost Piezoelectric Transducer and Advanced Signal Processing Techniques

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 2 ◽  
Author(s):  
Bruno A. Castro ◽  
Fabricio G. Baptista ◽  
José A. C. Ulson ◽  
Alceu F. Alves ◽  
Guilherme A. M. Clerice ◽  
...  
Keyword(s):  
Signal Processing ◽  
Piezoelectric Transducer ◽  
Acoustic Waves ◽  
Structural Damage ◽  
Damage Identification ◽  
Low Cost ◽  
Time Of Arrival ◽  
Damage Location ◽  
Processing Techniques ◽  
Signal Processing Techniques

The development of new low-cost transducers and systems has been extensively aimed at in both industry and academia to promote a correct failure diagnosis in aerospace, naval, and civil structures. In this context, structural health monitoring (SHM) engineering is focused on promoting human safety and a reduction in the maintenance costs of these components. Traditionally, SHM aims to detect structural damages at the initial stage, before it reaches a critical level of severity. Numerous approaches for damage identification and location have been proposed in the literature. One of the most common damage location techniques is based on acoustic waves triangulation, which stands out as an effective approach. This method uses a piezoelectric transducer as a sensor to capture acoustic waves emitted by cracks or other damage. Basically, the damage location is defined by calculating the difference in the time of arrival (TOA) of the signals. Although it may be simple, the detection of TOA requires complex statistical and signal processing techniques. Based on this issue, this work proposes the evaluation of a low-cost piezoelectric transducer to determine damage location in metallic structures by comparing two methodologies of TOA identification, the Hinkley criterion and the statistical Akaike criterion. The tests were conducted on an aluminum beam in which two piezoelectric transducers were attached at each end. The damage was simulated by pencil lead break (PLB) test applied at four different points of the specimen and the acoustic signals emitted by the damage were acquired and processed by Hinkley and Akaike criteria. The results indicate that, although both signal processing methodologies were able to determine the damage location, Akaike presented higher precision when compared to Hinkley approach. Moreover, the experimental results indicated that the low-cost piezoelectric sensors have a great potential to be applied in the location of structural failures.

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