Wavelet-based Signal Processing Technique Research Articles

Building occupancy estimation using microwave Doppler radar and wavelet transform

Room-level occupancy estimation is a critical input for real estate management, including building utilization optimization, energy-efficient building control, building security, and occupant health and well-being. Knowing how many people are using a room at a time can enable businesses to use their real estate, energy and human capital more efficiently, reducing operating expenses as well as carbon footprint. Radar-based occupancy estimation is attractive because it is unobtrusive and does not introduce the privacy issues brought with video imaging-based sensors. In this paper, we present an occupant estimation approach based on continuous wave Doppler radar and wavelet-based signal processing techniques. Theoretical background provides a rational for using a wavelet-based time-frequency approach, and comprehensive simulation and experimental results demonstrate the effectiveness of the proposed approaches on a data set that includes 1–10 occupants. These results indicate that Doppler radar with wavelet-based signal processing may be an effective tool for occupant count in smart building applications.

차량 창문 개폐 제어 사용자 인터페이스용 웨이블렛 기반 센서 신호처리 기술

차량 창문 개폐 제어 사용자 인터페이스용 웨이블렛 기반 센서 신호처리 기술

Investigation of the damage mechanisms for mode I delamination growth in foam core sandwich composites using acoustic emission

In this article, acoustic emission monitoring was used to identify different types of damage mechanisms which occur during delamination of sandwich structures. Features of the obtained acoustic emission signals and wavelet-based signal processing technique were utilized to discriminate failure mechanisms during the delamination test. Dominant damage mechanisms were classified based on their power spectral density in distinct frequency ranges and the energy distribution criterion in each component. The scanning electron microscopy was also employed to verify the results which were obtained from the acoustic emission investigation. It was concluded that frequency domain analysis and wavelet analysis are efficient tools for identification and discrimination of different damage mechanisms in sandwich structures.

Wavelet-based acoustic emission characterization of surface damages during experimental simulation of sheet metal forming process

In this study, acoustic emission monitoring with a wavelet-based signal processing technique is developed to detect different surface damage mechanisms during experimental simulation of sheet metal forming processes. Firstly, the obtained acoustic emission signals are decomposed into various wavelet levels. Each level includes detail and approximation that are called components and related to a specific frequency range. Secondly, the energy distribution criterion is applied to find more significant components each one of which is in relation to a distinct type of damage. Results showed that the energy of acoustic emission signals was concentrated in four significant components which can be attributed to three specific dominant phenomena occurred during different stages of a well-known surface damage, i.e. galling. Indeed to compare the results with previous investigations, surface observations were used to determine how the different surface damage mechanisms are related to the dominant corresponding wavelet components.

Oil Transmission Pipelines Condition Monitoring Using Wavelet Analysis and Ultrasonic Techniques

In this paper, a reliable and sensitive diagnostic method for oil pipelines based on wavelet analysis and ultrasonic technique. This will be achieved by the use of 3-D finite element modeling software (Abaqus CAE 6.10) combined with a power full wavelet based signal processing technique will be used to collect the empirical ultrasonic data to validate the developed diagnostic method. The affect known seeded faults i.e., 1 mmhole at 25%, 50%, 75% and 100% depth in pipe wall were investigated using FEM techniques. A developed acoustic transceiver (Air Ultrasonic Ceramic Transducer 235AC130) will be used to collect the empirical ultrasonic data to validate the developed diagnostic method. The amplitudes and frequency spectra of the ultrasonic signals were measured and the predicted results were found to be in good agreement with the measured data, and that to confirm that this method can provide important information on pipe defects.

Characterization of composite materials damage under quasi-static three-point bending test using wavelet and fuzzy C-means clustering

In this study, two types of specimens, woven [0, 90]s and unidirectional [0]s glass/epoxy composites, were studied. Acoustic emission (AE) monitoring with wavelet-based signal processing technique and Fuzzy C-Means (FCM) clustering method was used to detect the different fracture mechanisms during delamination in quasi-static three-point bending test. The three-point bending test simulates thrust force, the most effective factor in delamination, throughout the process of drilling without backup plate. The obtained AE signals were decomposed into various wavelet levels and classified using FCM. Scanning Electron Microscopy (SEM) observation was used to determine the different fracture mechanisms. The results show that the energy of AE signals has been concentrated in two significant components for both types of specimens, and the dependency percentage of damages in the two specimens is different.

Wavelet-based signal processing technique for multiple fault detection in induction motor: ANN approach

Faults and failures of induction motor can lead to excessive downtimes and generate large losses in terms of maintenance and revenue. This motivates motor monitoring, incipient fault detection and diagnosis. Most recurrent faults in induction motor are turn to turn short circuit, bearing deterioration, and cracked rotor bar. This paper presents a novel online methodology for multiple fault detection in induction motor. Motor line currents are recorded and analysed using modern signal processing tool and processed by applying discrete wavelet transformation. Feed forward ANN is used for fault characterisation based on DWT features extracted from motor current, which identifies bearing defects; interturn fault, broken rotor bar and voltage unbalance conditions in three phase induction motor. In order to demonstrate the efficiency of DWT detail analysis of fault classification is done based on time domain approach. Results are presented below to show the effectiveness of proposed methodology.

Wavelet-based acoustic emission characterization of damage mechanism in composite materials under mode I delamination at different interfaces

In this paper, acoustic emission (AE) monitoring with a wavelet-based signal processing technique is developed to detect the damage types during mode I delamination on glass/polyester composite materials. Two types of specimen at different midplane layups, woven/woven (T3) and unidirectional/unidirectional (T5), leading to different levels of damage evolution, were studied. Double cantilever beam (DCB) is applied to simulate delamination process for all specimens. Firstly, the obtained AE signals are decomposed into various wavelet levels. Each level includes detail and approximation that are called components and related to a specific frequency range. Secondly, the energy distribution criterion is applied to find the more significant components each one of which is in relation to a distinct type of damage. The results show that the energy of AE signals has been concentrated in three significant components for both of the specimens. There is a differ- ence in energy distribution of similar components of two specimens. It indicates that there is a dissimilar dominant damage mechanism for two different interfaces during the delamination process. Additionally, the microscopic observation (SEM) is used to determine how the different fracture mechanisms are related to the dominant corresponding wavelet components.

Testing and evaluation of water hydraulic components by acoustic emission and wavelet analysis

Abstract In condition monitoring and fault diagnosis of machinery, vibration analysis is the major method and wavelet analysis is widely implemented. The acoustic emission (AE) method is another important and effective signal in fault diagnosis of mechanical systems. In this paper, the AE signal is applied to diagnose the piston condition of the water hydraulic motor in a water hydraulic system. The AE signal is obtained under different flow rates and torques. The wavelet-based signal processing technique is used to analyze the fluid-borne vibration and structure-borne vibration (acoustic signal). The spectrum based on wavelet analysis is effective in analyzing the acoustic signals under specific frequency ranges, compared with the conventional power spectrum density (PSD). The results show root mean square (RMS) of the wavelet-based acoustic signal analysis is very effective for the fault diagnosis of piston cracks under the different flow rates and torques.

Electrode placement over the innervation zone affects the low-, not the high-frequency portion of the EMG frequency spectrum

The purpose of this study was to use a wavelet-based signal processing technique to examine the influence of electrode placement over the innervation zone (IZ) on the shape of the electromyographic (EMG) frequency spectrum. Ten healthy males (mean ± SD age = 23.6 ± 3.0 years) performed isometric muscle actions of the dominant leg extensors at 10%, 40%, 70%, and 100% of the maximum voluntary contraction (MVC). Surface EMG signals were detected simultaneously from the vastus lateralis with two bipolar electrode arrangements. One of the electrode arrangements had its center point located directly over the IZ, while the other arrangement had its center point distal to the IZ (i.e., 20 mm away). All EMG signals were processed with a wavelet-based procedure. The results showed that for all isometric torque levels, the EMG signals from the distal electrode arrangement demonstrated greater total intensity values than those for the IZ arrangement for frequencies ranging from approximately 2 to 110 Hz. There were no consistent differences, however, between the IZ and distal electrode arrangements for total EMG intensity values above 110 Hz. Thus, these findings indicated that electrode placement over the IZ affected primarily the low-, rather than the high-frequency portion of the EMG frequency spectrum.

Damage Detection in Composite Plates by Using an Enhanced Time Reversal Method

A damage detection technique, which does not rely on any past baseline signals, is proposed to assess damage in composite plates by using an enhanced time reversal method. A time reversal concept of modern acoustics has been adapted to guided-wave propagation to improve the detectability of local defects in composite structures. In particular, wavelet-based signal processing techniques have been developed to enhance the time reversibility of Lamb waves in thin composite laminates. In the enhanced time reversal method, an input signal at an excitation point can be reconstructed if a response signal measured at another point is reemitted to the original excitation point after being reversed in a time domain. This time reversibility is based on linear reciprocity of elastic waves, and it is violated when nonlinearity is caused by a defect along a direct wave path. Examining the deviation of the reconstructed signal from the known initial input signal allows instantaneous identification of damage without requiring the baseline signal for comparison. The validity of the proposed method has been exemplified through experimental studies on a quasi-isotropic laminate with delamination.

Time reversal active sensing for health monitoring of a composite plate

The applicability of a time reversal concept in modern acoustics to structural health monitoring was investigated. The time reversal method has been adapted to guided-wave propagation to improve the detectability of local defects in composite plate structures. Specifically, a wavelet-based signal processing technique has been developed to enhance the time reversibility of Lamb wave in thin composite plates. The validity of the proposed method is demonstrated through experimental studies in which input signals exerted at piezoelectric (PZT) patches on a quasi-isotropic composite plate are successfully reconstructed by using the time reversal method. The ultimate goal of this study is to develop a reference-free damage diagnosis technique based on the time reversal process so that defects can be identified without relying on any past baseline data.

An automated algorithm for determining respiratory rate by photoplethysmogram in children

We have developed an automated algorithm to allow the measurement of respiratory rate directly from the photoplethysmogram (pulse oximeter waveform). To test the algorithm's ability to determine respiratory rate in children. A convenience sample of patients attending a paediatric Accident and Emergency Department was monitored using a purpose-built pulse oximeter and the photoplethysmogram (PPG) recorded. Respiration was also recorded by an observer activating a push-button switch in synchronization with the child's breathing. The switch marker signals were processed to derive a manual respiratory rate that was compared with the wavelet-based oximeter respiratory rate derived from the PPG signal. Photoplethysmograms were obtained from 18 children aged 18 mo to 12 y, breathing spontaneously at rates of 17 to 27 breaths per minute. There was close correspondence between the wavelet-based oximeter respiration rate and the manual respiratory rate, with the difference between them being less than one breath per minute in all children. Our automated algorithm allows the accurate determination of respiratory rate from photoplethysmograms of a heterogeneous group of children. We believe that our automated wavelet-based signal-processing techniques could soon be easily incorporated into current pulse oximetry technology.

Fault Detection in a Water Hydraulic Motor Using a Wavelet Transform

This paper is concerned with the detection of a piston crack in a water hydraulic motor used in a fluid power system. The wavelet-based signal processing technique to detect a piston crack was studied. A complete procedure of wavelet-based vibration signal analysis was developed. A modified noise reduction method based on wavelet analysis for feature extraction of the impulse peak vibration excited by the piston was applied to the vibration data of a water hydraulic motor. A continuous wavelet transform (CWT) and a wavelet packet (WP) were applied to the analysis of the impulse vibration signals. The feature values of the peaks excited by the impulse vibration signals can be extracted by using WP to decompose and compress the de-noise signals. Moreover, the signal component indicative of a fault was identified through the analysis of the vibration signal in the time domain in wavelet analysis. This technique was shown to be a powerful tool for the fault detection of a water hydraulic motor.

Processing of Pulse Oximeter Data Using Discrete Wavelet Analysis

A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.

Wavelet-based active sensing for delamination detection in composite structures

In this paper a signal processing technique is developed to detect delamination oncomposite structures. In particular, a wavelet-based signal processing technique isdeveloped and combined with an active sensing system to produce a near-real-time, onlinemonitoring system for composite structures. A layer of piezoelectric patches is used togenerate an input signal with a specific wavelet waveform and to measure responsesignals. Then, the response signals are processed by a wavelet transform to extractdamage-sensitive features from the original signals. The applicability of the proposedmethod to delamination identification has been demonstrated by experimentalstudies of a composite plate under varying temperature and boundary conditions.

Wavelet-based AE characterization of composite materials

This paper addresses an application of recently developed wavelet-based signal processing technique on a composite fracture behavior study. Here, the wavelet methodology is introduced, and a complete procedure of wavelet-based acoustic emission (AE) analysis methods developed by the author are demonstrated. In this research, the AE signals from glass fiber reinforced (GFR) composites are collected during the standard quasi-static tensile testing and transformed by the Daubechies discrete wavelets using the programs developed by the author. Based on AE tests, the fracture behaviors are studied for the material. In the study of GFR composite fracture behavior, classical linear fracture mechanics method is used for comparison. The exponential constant m value used to determine the relationship between stress and stress intensity factor are compared relative to the classical fracture mechanics and the AE techniques. In order to demonstrate the advantage of the wavelet-based AE techniques, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the wavelet-based method better approximates residual strength relative to the classical AE techniques. In this paper, the results of the wavelet-based AE analysis of glass fiber composites are presented. The results indicate that wavelets-based signal processing is an efficient tool in the analysis and the transformation of AE signals.