Motion Detection for a Precision Positioning System by Wavelet Analysis

1997 ◽  
Author(s):  
Hung-Ming Tai ◽  
Cheng-Kuo Sung
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Motion Detection ◽  
Experimental Setup ◽  
Positioning System ◽  
Precision Positioning ◽  
Band Energy ◽  
Pitch Motion ◽  
The Difference ◽  
Short Time

Abstract The defects due to wear or improper assembly processes may induce undesirable vibrations that in turn cause mechanical failures. This paper employs Wavelet Transform to analyze the vibration of the pitch motion of the carriage in a precision positioning system. Two accelerometers are attached to the carriage at different locations and the accelerations are used to investigate the vibration of the pitch motion. By using Wavelet Transform, the signal measured from the proposed experimental setup are appropriately processed and used for the diagnosis of the defects. The percentage of the band energy (PBE)is found to be a helpful reference for detecting the abnormal conditions. This method successfully combines the techniques of measurement with the theory of the wavelet analysis in monitoring a precision positioning system. The difference between the results obtained both from the Short-Time Fourier analysis and wavelet analysis is also discussed.

Crack Location in Beams Using Wavelet Analysis

2013 ◽  
Vol 569-570 ◽  
pp. 1021-1028 ◽  
Author(s):  
Mario Solís ◽  
Mario Algaba ◽  
Pedro Galvín
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Curve Fitting ◽  
Spline Interpolation ◽  
Mode Shapes ◽  
Steel Beams ◽  
Crack Location ◽  
Interpolation Technique ◽  
The Difference ◽  
Spline Interpolation Technique

This paper applies a methodology for damage detection in beams proposed by the authors. The methodology is based on a continuous wavelet analysis of the difference of mode shapes between a damaged state and a reference state. The wavelet transform is used to detect changes in the mode shapes induced by damage. The wavelet coefficients for each mode are added up and normalized to unity in order to obtain a clear and precise damage assessment. A curve fitting approach reduces the effect of experimental noise in the mode shapes. When only a small number of measuring points are available, a cubic spline interpolation technique provides additional “virtual” measuring points. The interpolation technique may also be used when measuring points are not equally spaced. It also serves as a softening technique of the mode shapes when applied, and no curve fitting approach is used in that case. An antisymmetric extension at both ends of the mode shapes is used to avoid the edge effect in the wavelet transform. The paper presents the results obtained for steel beams with an induced crack. Several sizes and locations of the crack have been considered. The paper addresses several issues affecting the accuracy of the proposed methodology, such as the number of measuring points and the effect of the extension, curve fitting and interpolation techniques.

The Model of Short-Time Traffic Flow Prediction on High-Grade Highway

2011 ◽  
Vol 255-260 ◽  
pp. 4128-4132
Author(s):  
Hong Chen ◽  
Yu Wei Yuan ◽  
Juan Sun ◽  
Na Bao
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Traffic Flow ◽  
Rbf Neural Network ◽  
High Grade ◽  
Traffic Flow Prediction ◽  
Discontinuous Flow ◽  
Flow Prediction ◽  
The Difference ◽  
Short Time

In order to study the short-time traffic flow prediction on high-grade highway, the article proposed a model based on wavelet analysis and RBF neural network. Aiming to the traffic flow’s characteristic of highway, the study focus on three facet: network topology, the difference of continuous flow and discontinuous flow , the flow of lanes’ uplink and downlink are not equal. Thus the article use the wavelet analysis to do data preprocessing, then structure the model of short-term traffic flow prediction based on RBF neural network. The experiment result shows that the new hybrid model adapt to high-grade highway, and model considering traffic flow characteristic is better than the model which is not. Meanwhile the model has the higher precision of prediction.

scholarly journals Wavelets for EEG Analysis

2020 ◽  
Author(s):  
Nikesh Bajaj
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Time Frequency ◽  
Analysis Technique ◽  
Artefact Removal ◽  
Short Time ◽  
Electroencephalogram Eeg

This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.

Dynamic Analysis of Faulty Rotors through Signal Processing

2016 ◽  
Vol 852 ◽  
pp. 602-606
Author(s):  
Cherukuri Bhargav Sai ◽  
D. Mallikarjuna Reddy
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Dynamic Analysis ◽  
Mode Shape ◽  
Wavelet Coefficients ◽  
Nodal Displacement ◽  
Rotating Shafts ◽  
The Difference ◽  
Shape Data

In this study, an effective method based on wavelet transform, for identification of damage on rotating shafts is proposed. The nodal displacement data of damaged rotor is processed to obtain wavelet coefficients to detect, localise and quantify damage severity. Because the wavelet coefficients are calculated with various scaled indices, local disturbances in the mode shape data can be found out in the finer scales that are positioned at local disturbances. In the present work the displacement data are extracted from the MATLAB model at a particular speed. Damage is represented as reduction in diameter of the shaft. The difference vectors between damaged and undamaged shafts are used as input vectors for wavelet analysis. The measure of damage severity is estimated using a parameter formulated from the distribution of wavelet coefficients with respect to the scales. Diagnosis results for different damage cases such as single and multiple damages are presented.

Destruction of Actin Filaments by Osmium Tetroxide

1977 ◽  
Vol 35 ◽  
pp. 466-467
Author(s):  
P. Maupin-Szamier ◽  
T. D. Pollard
Keyword(s):  
Actin Filaments ◽  
Time Course ◽  
Osmium Tetroxide ◽  
Rabbit Muscle ◽  
Muscle Actin ◽  
Sodium Phosphate Buffer ◽  
Transmission Electron ◽  
The Difference ◽  
Rates Of Decay ◽  
Short Time

We have studied the destruction of rabbit muscle actin filaments by osmium tetroxide (OSO4) to develop methods which will preserve the structure of actin filaments during preparation for transmission electron microscopy.Negatively stained F-actin, which appears as smooth, gently curved filaments in control samples (Fig. 1a), acquire an angular, distorted profile and break into progressively shorter pieces after exposure to OSO4 (Fig. 1b,c). We followed the time course of the reaction with viscometry since it is a simple, quantitative method to assess filament integrity. The difference in rates of decay in viscosity of polymerized actin solutions after the addition of four concentrations of OSO4 is illustrated in Fig. 2. Viscometry indicated that the rate of actin filament destruction is also dependent upon temperature, buffer type, buffer concentration, and pH, and requires the continued presence of OSO4. The conditions most favorable to filament preservation are fixation in a low concentration of OSO4 for a short time at 0°C in 100mM sodium phosphate buffer, pH 6.0.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

Identification of Static Unbalance Wheel of Passenger Car Carried out on a Road

2014 ◽  
Vol 214 ◽  
pp. 48-57 ◽  
Author(s):  
Krzysztof Prażnowski ◽  
Sebastian Brol ◽  
Andrzej Augustynowicz
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Constant Velocity ◽  
Fourier Transforms ◽  
Static Condition ◽  
Rotational Frequency ◽  
Acceleration Sensor ◽  
Short Time Fourier Transform ◽  
Road Roughness ◽  
Short Time

This paper presents a method of identification of non-homogeneity or static unbalance of the structure of a car wheel based on a simple road test. In particular a method the detection of single wheel unbalance is proposed which applies an acceleration sensor fixed on windscreen. It measures accelerations cause by wheel unbalance among other parameters. The location of the sensor is convenient for handling an autonomous device used for diagnostic purposes. Unfortunately, its mounting point is located away from wheels. Moreover, the unbalance forces created by wheels spin are dumped by suspension elements as well as the chassis itself. It indicates that unbalance acceleration will be weak in comparison to other signals coming from engine vibrations, road roughness and environmental effects. Therefore, the static unbalance detection in the standard way is considered problematic and difficult. The goal of the undertaken research is to select appropriate transformations and procedures in order to determine wheel unbalance in these conditions. In this investigation regular and short time Fourier transform were used as well as wavelet transform. It was found that the use of Fourier transforms is appropriate for static condition (constant velocity) but the results proves that the wavelet transform is more suitable for diagnostic purposes because of its ability of producing clearer output even if car is in the state of acceleration or deceleration. Moreover it was proved that in the acceleration spectrum of acceleration measured on the windscreen a significant peak can be found when car runs with an unbalanced wheel. Moreover its frequency depends on wheel rotational frequency. For that reason the diagnostic of single wheel unbalance can be made by applying this method.

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