New Method for Transient Signal Analysis using the Continuous Wavelet Transform

We are investigating countermeasure techniques against electric noise on telecommunication and related equipment. There are many types of electric noise, and we focus on the characterization of transient noise such as the switching noise of switching power supplies. We are developing a method of transient signal analysis based on linear predictive coding (LPC) for the purpose. Unfortunately, as standard LPC contains Yule-Walker (YW) approximation, which replaces a given time series with a periodic time series for simplification, it is not suitable for transient signal analysis. Therefore, our method of transient signal analysis does not contain YW approximation. Instead, we use the local linearization technique, which was developed in the field of quantum mechanics. Our method makes it possible to obtain instantaneous frequencies and instantaneous decay rates simultaneously with high precision from a small number of samples. We discuss our LPC method avoid using YW approximation and using the local linearization technique and its application to repetitive ringing signals, which mimics the switching noise of a switching power supply, to show the efficiency of our method.

Time–frequency analysis (TFA) methods can characterize the instantaneous frequency (IF) features of nonstationary signals by constructing the time-domain signal model. However, the traditional TFA methods are ineffective for transient signals that are discontinuous in the time domain and characterized by sharp frequency variation. Therefore, this article proposes a new TFA method called generalized transient-squeezing transform (GTST) to process transient signals by constructing a signal model in the frequency domain. First, the generalized group delay (GD) estimation is calculated based on the frequency-domain signal model. Then, the generalized transient-squeezing operator is constructed by iterative operations. Finally, the time–frequency (TF) energy is rearranged according to the constructed operator. The GTST provides a more compact TF representation (TFR) of the transient signal while maintaining reversibility. Numerical validation shows that the proposed method has higher TF concentration and accuracy in analyzing transient signals compared with other advanced TFA methods. Experimental validation shows the effectiveness of the proposed method in practical transient signal analysis.

P01-Comparison of short-time Fourier transform and continuous wavelet transform for frequency analysis of sleep EEG

Frequency domain analyses in electromyographic (EMG) signals are frequently applied to assess muscle fatigue and similar variables. Moreover, Fourier-based approaches are typically used for investigating these procedures. Nonetheless, Fourier analysis assumes the signal as stationary which is unlikely during dynamic contractions. As an alternative method, wavelet-based treatments do not assume this pattern and may be considered as more appropriate for joint time-frequency domain analysis. Based on the previous statements, the purpose of the present study was to compare the application of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to assess muscle fatigue in dynamic exercise of a 1-km of cycling (time-trial condition). The results of this study indicated that CWT and STFT analyses have provided similar fatigue estimates (slope) (p> 0.05). However, CWT application represents lesser dispersion (pp> 0.05) according to different methods, it is important to note that these responses seem to show greater values for CWT compared to STFT for 2 superficial muscles. Thereby, we are capable of considering CWT as a reliable and useful method to take into consideration when non-stationary or oscillating exercise models are evaluated.

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in supramaximal constant load dynamic exercise (110% VO(2peak)). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in supramaximal constant load dynamic exercise (P>0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P < 0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

The Fourier transform is widely accepted as the time-to-frequency conversion procedure, although it has some limitations. Currently, measurements in the time domain are usually transient (non-periodic waveforms) within a finite window time and discrete (non-continuous) sampled signals. The accuracy of the Fourier transform decreases as the window time and sampling frequency decrease. This is where the wavelet transform proves to be a valuable tool in this analysis. This paper presents a novel method for estimating the complex electrical impedance of power transformers by analyzing transient electrical signals with the continuous wavelet transform. The great importance of knowing the complex electrical impedance of the transformer is that it allows knowing the state and condition of the internal parts, such as the core and the windings, whose behavior depends on the frequency with which the transformer is fed. The wavelet transform is employed in the proposed method to improve the analysis of the frequency response (FRA), following the same procedure commonly used with the Fourier transform. The proposed method is validated by performing an experimental test on a 28 MVA power transformer. The results show that the new method using the continuous wavelet transform is a power tool that enhances the extraction of the total electrical impedance curve (magnitude–phase) compared to the Fourier transform. This enables real-time frequency response analysis in transformers, facilitating accurate diagnosis.

The research and application actuality of wavelet post-analysis methods in electrical transient signal analysis was introduced, and several wavelet post-analysis methods were presented, then wavelet average entropy and wavelet distance entropy were taken as examples to show its feasibility, simulation results showed that wavelet entropy is effective for fault detection, which also opened new train of thoughts on fault classification. Finally, application of several post-analysis methods in power system transient signal was discussed, which showed the importance of wavelet post-analysis

Improving the spatial resolution in CZT detectors using charge sharing effect and transient signal analysis: Simulation study

Comparison of spectral decomposition methods

A low cost indirect type AC voltage regulating system is proposed to overcome the grid pollution of silicon controlled regulating circuit and the high cost of chopping sine wave regulating circuits. The system consists of full bridge rectifier, filter and full bridge inverter. The bipolar SPWM control signal for inverter is generated by the STM32 micro controller. The circuits is developed and used in the dimming of 400 W high pressure sodium lamp inductive loads. Continuous dimming with slight distortion of the sine wave is obtained. The system is attractive in the replacement of silicon controlled dimming circuit, and promotes the energy saving project. Introduction China's urbanization continues to accelerate, owning to people’s greatly improved living standard and the development of the economy. As an important indicator of urban modernization, traffic lighting and landscape lighting is developing rapidly and consuming more and more energy. Energy shortage and environmental pollution problems is a common concern of all humanity, energy and environmental protection are increasingly being taken seriously. According to the external light intensity and flow of people, appropriate steps down lightening, can effectively save energy, thus reduce carbon emissions [1]. The AC regulating system is the core block for light dimming. The AC regulating circuit based on Silicon Controlled Rectifier (SCR) is used widely in dimming [2], with advantages of low cost and simple structure. However, the SCR based dimming circuit adjusts the output voltage by changing the conduction angle of SCR. Thus the output is not a standard sine wave, but contains a large number of high-order harmonics, which will disturb other equipment’s in the grid. With the development of electronic devices IGBT, dimming circuit based on chopping is proposed and used in the stage dimming, as well as hotels [3]. The output is pure sine wave and will not affect other equipment’s in the grid any more. However, the chopping based dimming circuit is relatively complex and the filter circuit depends on the load characteristics, which resulting in higher system costs and can only be applied in expensive entertainment places [4]. In this paper, a low cost AC regulating system with simple structure is proposed and realized. The output is continuously adjustable standard sine wave, and compatible for resistive, inductive and capacitive loads. System Design The system consists of full bridge rectifier, full-bridge inverter and filer circuit, as shown in Fig. 1. The input 220V/50HZ sine wave is rectified to DC voltage firstly, and then enters into the fullbridge inverter circuit and filter circuit, where the input DC voltage is inverted to AC voltage. The output voltage is adjusted by changing the control signal of full-bridge inverter. The output is insensitive with the load characteristics, owning to the inverter circuit. International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 996 Fig. 1: Schematic of the indirect regulating system The full-bridge inverter is the core block of the system. The inverter consists of 4 seriesand parallel -connected IGBT devices, as shown in Fig. 2. The 4 IGBTs are driven by SPWM signal generated by the STM32 microcontroller. According to detected voltage and current of the input and output ports, the STM32 microcontroller dynamically changes the SPWM driving signal to satisfy the output voltage. These are two types of SPWM driving signal, unipolar SPWM and bipolar SPWM. For inductive load in our application, bipolar SPWM is chosen for driving the IGBTs, since it is far easier to get a good output waveform. In Fig. 2, the left half-bridge consists of IGBT T1 and T, and the right half-bridge consists of IGBT T3 and T4. For bipolar IGBT driving circuit, the high-side and low-side IGBTs (e.g. T1 and T2) have complementary driving signal, and the diagonal IGBTs (e.g. T1 and T4) have the same driving signal. The output is close the peak of the sine signal as the duty cycle of the SPWM is close to 0% or 100%, meanwhile the output is close to 0 as the duty cycle of the SPWM is close to 50%, which is shown in Fig. 3. To simplify the system complexity and lower the cost, two complementary driving signals with dead zone are generated by the STM32. Fig. 2: Topology of full-bridge inverter Fig. 3: Bipolar SPWM driving signal T1

The proposed technique consists of a pre-processing unit based on Continuous Wavelet Transform (CWT) in combination with an Artificial Neural Networks (ANN). CWT acts as an extractor of distinctive features in the transient current signals at the sending and receiving end of the transmission line. This information is then fed to the ANN for detecting the fault type and location of fault. The results presented clearly indicate that the present technique is very fast, computationally efficient and intelligent enough to accurately identify three different types of fault (LG & LLG) and their locations.

Large-scale AC-DC hybrid power systems have become a basic pattern for power transmission in China. Currently, time-domain simulation is widely used for transient stability analysis, which has the disadvantages of high computational burdens and lack of system stability margin information. An alternative method for transient stability analysis is the direct method, which assesses transient stability from the energy point of view. Direct method requires much less computation efforts. In addition, the stability margin of the system can be obtained. Due to the challenges in system modelling, energy function construction, and determination of critical energy value, it is challenging to apply direct method for transient stability analysis of AC-DC hybrid power systems. This paper is a step in exploring the application of direct method in AC-DC hybrid power systems.

Chronobiology, which studies periodic patterns or rhythms in living beings, often needs to characterize the observed chronobiological time series (CTS) and to study the stability and adaptability of the periodic patterns in different environmental conditions. Fourier transform (FT) based methods and the complex demodulation (CD) approach have been widely used in such studies. However, the former lacks temporal resolution and the later needs to extract the temporal behaviors of individual frequencies. In this paper, we propose a new approach to characterize the CTS based on the continuous wavelet transform (CWT). It allows us to investigate the time-frequency dynamics of different rhythmic-band activities in the CTS simultaneously. Two application results are presented to illustrate the proposed method.

This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computer-based machine health monitoring solution.

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup.