Signal Processing In Time-frequency Domain Research Articles

Influence of Meteorological Processes on cGPS Measurements of Crustal Movements

Surface displacement measurements collected using continuous Global Positioning System (cGPS) stations include the contribution of both endogenic processes (e.g., plate tectonics, isostasy) and exogenic processes that show seasonal variation (e.g., climate). These seasonal variations distort the tectonic signals and limit the usefulness of short-term investigations. This paper explores cGPS and meteorological time series collected in southern Africa and investigates whether the usefulness of cGPS time series can be improved by accounting for the seasonal effects of the dominant meteorological processes. The study is carried out using time, frequency, and time-frequency domain signal processing (inferential) analysis techniques. It was found that from the considered atmospheric processes, thermally-induced effects, which are not corrected for in the cGPS data, are the most prominent meteorological contributors in the vertical annual deformation component observed in cGPS time series. The effects of heave action (due to seasonal subsurface water infiltration and absorption, as well as changes in the water table) and changes of water mass distribution (caused by infiltration, transportation, extractions, and evaporation) on the displacement time series are much smaller. This suggests that correcting for thermal expansion, contraction, and thermal-induced errors could reduce the annual seasonal deformation component observed in cGPS position measurements in southern Africa and, most probably, in other parts of the world subjected to large seasonal variations in atmospheric temperatures. Reducing the magnitude of the seasonal components would increase the usefulness of short-term cGPS campaigns.

Large Area Detection of Microstructural Defects with Multi-Mode Ultrasonic Signals

Cyclic loading or other stresses can lead to development of cracks and crack growth in mechanical structures, leading to eventual failure. While ultrasound imaging can be used for non-destructive testing of such structures, conventional ultrasound techniques are often limited by crack size, density, and areal coverage. An effective characterization of real-world, large-area structures is required at an early damage stage to prevent catastrophic failure and predict remaining life. In this study, a new nonlinear ultrasonic testing (NUT) method is proposed for large-area monitoring of practical structures with arbitrary complexity by using multiple-mode guided-wave ultrasonic signals. The proposed guided-wave NUT technique requires single-element transducers, simple electronics, and a mixed time-frequency domain signal processing. As a proof-of-concept demonstration, numerical simulations and experiments are performed on an A36 carbon steel beam assembly with previously formed microstructural defects that cause nonlinearities in ultrasonic response. The quadratic dependence of the nonlinear wave excitation on the input ultrasonic signal amplitude is shown by numerical simulations, and such a nonlinear ultrasonic response is experimentally observed in the zone with a high density of microstructural defects.

2Pb5-5 Time-Frequency Domain Signal Processing for 3D Acoustic Impedance Microscopy and Its Application to Human Skin Observation

2Pb5-5 Time-Frequency Domain Signal Processing for 3D Acoustic Impedance Microscopy and Its Application to Human Skin Observation

Development and Testing of a Combined Machine and Process Health Monitoring System

Process monitoring has been shown to be capable of observing the quality of a machining operation through sensor signals and analysis in both the literature and in commercially available systems. Some of these systems provide an additional benefit of monitoring the health of a machine tool. However, the commercially available systems tend to utilise relatively simple analysis techniques for both the process and machine health, limiting their application and robustness. Industrial interest in systems that can profit from the current advances in machine tool digitalisation and data analytics has grown considerably. This is especially true for the capability of early-detection of quality issues in components, whilst also ensuring machine tools are in a condition that can achieve high quality production. The present research includes the development and testing of a fingerprint routine which can be run at regular intervals to detect potential failure modes or machine tool degradation through signal analysis. Machining trials were carried out with the objective of detecting known defects in a workpiece through signal analysis. For both cases, a combined monitoring system was developed for data capture during testing, and a number of failure modes and defects were physically simulated to test the possibility of detection in the acquired signals. Time domain, frequency domain, and time-frequency domain signal processing techniques were applied to the sensor data with various levels of success. Continuous wavelet transforms (CWT) were of particular interest, as they successfully captured signal changes between tests for the physically simulated failure modes of the machine tool and the component. Therefore, a comparative CWT analysis was developed which successfully emphasised some of the machine tool failure modes and part defects when compared to baseline signals. The output of the comparative analysis may be well-suited to automation through machine learning techniques.

Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan

For control of real-time Internet of Things (IoT)-based remote welding process, continuous detection of defects occurring in the weld sample is of utmost importance so that welding parameters can be changed accordingly to avoid further occurrence of such defects. Time-frequency domain signal processing method, such as discrete wavelet transform (DWT), can be applied for detection of such defects. DWT continuously decomposes a signal into detailed and approximate coefficients through its associated filter banks and provides a time-frequency domain representation of a signal. Different levels of decomposition capture different frequency components, and hence, there is a need for optimization of the level of decomposition of force and power signals recorded during joining of two aluminum sheets by friction stir welding (FSW), for correct identification and localization of defects occurring in the process. Internal defects in the weld samples are further verified by CT scan images. Statistical tools have been used to study the variations in the DWT coefficients due to both internal and surface defects. An attempt has been made to compare between force and power signals as to which gives better defect detection.

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

A General Method for Calculation of Depth of Anesthesia

This paper investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. Compared with the Bispectral Index (BIS), time-frequency domain signal processing technique and nonlinear dynamical analysis were combined for DOA assessment, multiple features were extracted from EEG, Lasso and Logistic regression were used to classify and calculate the index of DOA and evaluate its relationship with EEG features. In emulation and clinical practice, the index of DOA is very close to BIS. This method can enhance existing monitoring devices and work as a general method to find effective features for calculation of DOA.

Time–frequency feature representation using energy concentration: An overview of recent advances

Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time–frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time–frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time–frequency representations, and feature classifiers. The results of the literature review indicate that time–frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future.

Area-Power Efficient VLSI Implementation of Multichannel DWT for Data Compression in Implantable Neuroprosthetics

Time-frequency domain signal processing of neural recordings, from high-density microelectrode arrays implanted in the cortex, is highly desired to ease the bandwidth bottleneck associated with data transfer to extra-cranial processing units. Because of its energy compactness features, discrete wavelet transform (DWT) has been shown to provide efficient data compression for neural records without compromising the information content. This paper describes an area-power minimized hardware implementation of the lifting scheme for multilevel, multichannel DWT with quantized filter coefficients and integer computation. Performance tradeoffs and key design decisions for implantable neuroprosthetics are presented. A 32-channel 4-level version of the circuit has been custom designed in 0.18-mum CMOS and occupies only 0.22 mm(2) area and consumes 76 muW of power, making it highly suitable for implantable neural interface applications requiring wireless data transfer.