Signal Envelope Research Articles

Envelope Analysis of the Human Alpha Rhythm Reveals EEG Gaussianity.

To assess the Gaussianity of the human alpha rhythm using the envelope signal and the coefficient of variation of the envelope (CVE). Envelope analysis relies on the fact that the CVE for Gaussian noise is √{(4-π)/π} ≈ 0.523. CVE can thus be used as a hallmark to detect Gaussianity, and any significant deviation from Gaussianity can be linked to synchronous neural dynamics. We applied envelope analysis to EEG and iEEG public databases. The human alpha rhythm can be characterized either as a synchronous or as a Gaussian signal based on the value of its CVE. Furthermore, Fourier analysis showed the canonical spectral peak at ≈ 10[Hz] is present in both the synchronous and Gaussian cases, thus demonstrating this same peak can be produced by different underlying neural dynamics. Human EEG can be classified using envelope parameters. This study confirms the original interpretation of Adrian regarding the origin of the alpha rhythm but also opens the door for the study of Gaussianity in brain dynamics. Envelope analysis constitutes a novel complement to Fourier-based methods for neural signal analysis relating amplitude modulations (CVE) to signal energy. These results suggest a broader interpretation for event-related synchronization/desynchronization (ERS/ERD) may be needed.

Enhancing the Dynamic Range of OFDM Radars Using Non-Linear Operation and Symbol-Based Equalization

Due to the increased flexibility in terms of waveform generation, digitally-modulated radar systems are gaining increasing popularity among research and academia. The ability to dynamically allocate frequency and time slots, as well as to employ different coding, enables novel multiplexing techniques for multiple-input-multiple-output radars and sensor networks, compared to state-of-the-art chirp-modulated systems. Another significant benefit is the possibility for joint radar and communication with the same hardware. A popular modulation for digital radar is orthogonal frequency-division multiplexing (OFDM), due to its well-known properties from communication applications. It offers all the above-mentioned possibilities, however OFDM radar has two main drawbacks: high peak-to-average power ratio (PAPR) due to the varying signal envelope which significantly increases the linearity requirements on the analog hardware and reduces the power efficiency, as well as a decoupling of target range and frequency-shift, which eliminates the possibility of using a high-pass filter to suppress close targets and thus decreases the sensor's dynamic range in comparison to conventional frequency-modulated continuous-wave (FMCW). The latter weakness is sometimes also viewed as a positive, due to the fact that range-frequency coupling can lead to undesired effects in some cases. This work addresses both of these problems together, which exist in every real-world OFDM radar sensor, and presents detailed analysis, simulation, characterization and measurement validation results that show how these effects negatively influence the signal quality and the obtained range-Doppler map of an automotive radar. Furthermore, a non-linear operation mode with receiver-sided healing that does not require any additional processing steps or computation power during radar operation is proposed. Its high potential is verified by measurements with a 78 GHz prototype setup in an anechoic chamber with static reflectors, as well as an automotive radar echo generator for dynamic targets and extended ranges.

A monolithic gallium nitride‐based two‐phase synchronous buck converter with high operating frequency

AbstractThe need for a high‐frequency and high‐efficiency gallium nitride (GaN)‐based buck converter that can be controlled directly by a low swing pulse width‐modulated (PWM) signal, without the need for additional buffers or preamplifiers, poses a significant challenge in the field of power conversion and dynamically power supply applications. To solve this problem, this letter presents a monolithic two‐phase synchronous buck converter, fabricated using a 0.25‐um GaN‐on‐Si process, integrated both the drivers and power stage transistors, without requiring additional buffers or preamplifiers. So, the proposed converter can be directly controlled by a PWM signal with a swing of only 1 V (−0.5 to 0.5 V). At a 100‐MHz switching frequency, the converter achieves a maximum output power of 12 W with a power stage efficiency of 82%. The converter includes two identical half‐bridge Monolithic Microwave Integrated Circuit (MMICs), and with the frequency multiplication property of multi‐phase topology, the equivalent switching frequency can reach 200 MHz in the open‐loop operating mode. The experimental results show that the converter can track a 40‐MHz bandwidth envelope signal (256 QAM, 6 dB PAPR) with 80.1% efficiency.

Self-Vernier Effect-Assisted Optical Fiber Sensor Based on Microwave Photonics and Its Machine Learning Analysis

Optical Vernier effect has been recently demonstrated as a tool to enhance the sensitivity of optical fiber interferometric sensors and has become a hot topic in the last few years. The generation of the Vernier effect relies on the superposition of interferograms of two interferometers (a sensing one and a reference one) with marginally different optical path differences (OPDs), where an amplitude modulation-like signal is sustained in the output spectrum. The Vernier modulation envelope exhibits significantly magnified sensitivity in response to external perturbations, compared to the individual sensing interferometer, providing a new route to new generations of ultra-sensitive optical fiber sensors. However, the construction of a Vernier effect-based sensor needs careful integration of two interferometers with a precise OPD deviation, which is challenging if not impossible sometimes. Additionally, the interrogation of a Vernier effect-based sensor requires the acquisition of its spectrum in a broad wavelength (frequency) window with a large number of sampling points, followed by cumbersome processing for the extraction of the Vernier envelope signal. In this article, we propose a new concept of the self-Vernier effect for the sensitivity improvement of optical fiber sensors. A proof-of-concept demonstration is performed using a single low-coherent microwave-photonic Fabry-Perot interferometer (FPI) with an addition of an auxiliary delay path. The self-Vernier effect is obtained by the overlap between the microwave interferogram of the FPI and its slightly time-delayed one. Detailed analyses regarding the principle, mathematical modeling, and experimental investigation are given. Moreover, we propose and demonstrate a new demodulation approach to the Vernier effect-based sensor through machine learning analysis by directly and statistically learning the embedded relation between the measurand of interest and the output spectrum. It is appreciated that the necessity of the frequency observation window and the number of sampling points can be remarkably reduced through machine learning analysis. The present interrogation strategy is highly generalizable and can be readily applied to optical-domain Vernier effect-based optical fiber sensors.

Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis

The vibration signal of a faulty rolling bearing exhibits typical non-stationarity – often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of envelope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods.

Why EMD and similar decompositions are of little benefit for bearing diagnostics

Empirical mode decomposition (EMD) is a way of decomposing complex signals into a sum of “mono-components”, i.e. intrinsic mode functions (IMFs), each of which can be considered as a single carrier frequency, modulated in both amplitude and phase/frequency (an analytic signal), so that both amplitude and phase remain continuous and differentiable, the latter to instantaneous frequency. In many cases, the instantaneous frequency and amplitude could be valuable diagnostic features, for example with gear vibrations. Many authors have suggested applying this technique to the diagnostics of rolling element bearings (REBs), but this paper shows that REB signals intrinsically do not have such properties, except perhaps in short bursts, so that the excessive computation time trying to ensure continuous phase is wasted. Much more appropriate methods exist to extract the optimum envelope signal, which contains the desired diagnostic information, based on more relevant criteria. As its name suggests, the EMD process is empirical, with no analytical solution, so that different results can be obtained from different segments of the same signal, partly because of different noise content. Another way of obtaining IMFs is by using variational mode decomposition (VMD), which does have an analytical solution, making it less sensitive to noise, but still assumes that the signal can be validly decomposed into mono-components, which is not the case for bearing signals. The EMD process has two additional problems which make difficult its use for REB signals; (1) end effects, which usually mean that many, perhaps a majority, of IMFs are required purely to compensate for them, and which cannot be truncated for signals consisting of short bursts with sections of noise between them; and (2) mode mixing, meaning that repeatable results cannot be guaranteed, even for valid mono-components, let alone REB signals which are of a stochastic nature. The paper explains in detail why REB signals cannot be validly decomposed by EMD and similar methods, to give sub-components with properties directly related to bearing faults, even though many existing papers describe how the IMFs found by the EMD process might be selected or re-combined to have properties of interest in bearing diagnostics. In particular, the paper shows that the equivalent bandpass filters given by EMD have much poorer selectivity than most alternative filters. The paper uses many simulated and measured examples and demonstrations, to discuss these claims, including a number based on results from already published papers.

Orthodontic treatment of children with anterior open bite and posterior crossbite: An analysis of the stomatognathic system.

Dental malocclusions are deviations from normalities due to the inadequate growth and development of the dental arch which provides functional changes to the stomatognathic system. The aim of this longitudinal study was to evaluate the electromyographic activity (EMG) the masseter and temporalis muscles, strength of the orofacial tissues and occlusal force of children with anterior open bite (n=15) and posterior crossbite (n=20), 7 days after the removal of the orthodontic apparatus. A fixed horizontal palatal crib was used in the treatment of anterior open bite and the fixed appliances Hyrax or MacNamara was used in the treatment of posterior crossbite. EMG of the masticatory muscles was recorded using an electromyograph with wireless sensors during mandibular tasks. Habitual chewing was assessed using the integral of the linear envelope of the electromyographic signal in the masticatory cycles. The strength of the tongue and facial muscles was measured using the Iowa Oral Pressure Instrument. T-Scan was used to analyze the force of occlusal contact. Molar bite force was measured by digital dynamometer. Significant differences (p<0.05) were found in the EMG data of the masseter and temporalis muscles in the static and dynamic mandibular tasks. There were no significant difference in strength of orofacial tissues, occlusal contact force and molar bite force 7 days after the removal of the orthodontic apparatus. The results of this study suggest that the orthodontic treatment of anterior open bite and posterior crossbite in children promoted functional alteration in the electromyographic activity of masseter and temporalis muscles.

A Unification of LoS, Non-LoS, and Quasi-LoS Signal Propagation in Wireless Channels

The modeling of wireless communication channels is often broken down into two distinct states, defined according to the optical viewpoints of the transmitter (TX) and receiver (RX) antennas, namely, line-of-sight (LoS) and non-LoS (NLoS). Movement of the TX, RX, both and/or objects in the surrounding environment means that channel conditions may transition between LoS and NLoS leading to a third state of signal propagation, namely, quasi-LoS (QLoS). Unfortunately, this state is largely ignored in the analysis of signal propagation in wireless channels. We, therefore, propose a new statistical framework that unifies signal propagation for LoS, NLoS, and QLoS channel conditions, leading to the creation of the three-state model (TSM). The TSM has a strong physical motivation, whereby the signal propagation mechanisms underlying each state are considered to be similar to those responsible for Rician fading. However, in the TSM, the dominant signal component, if present, can be subject to shadowing. To support the use of the TSM, we develop novel formulations for the probability density functions of the in-phase and quadrature components of the complex received signal, the received signal envelope, and the received signal phase. In addition, we derive an expression for the complex autocorrelation function of the TSM, which will be of particular importance in understanding and simulating its time correlation properties. Finally, we show that the TSM provides a good fit to field measurements obtained for two different body-centric wireless channels operating at 2.45 GHz, which are known to be subject to the phenomena underlying the TSM.

Lateral M-Mode: Ultrasound Visualization of Displacement Along Longitudinal Direction at Intima-Media Complex.

Quantification of the dynamics of the carotid artery wall is useful in evaluating arteriosclerosis and atherosclerosis. As the carotid artery wall moves not only in the radial direction but also in the longitudinal direction, longitudinal movement should be considered in the analysis of the dynamic properties of the carotid artery wall. In this study, we propose a "lateral M-mode" method for visualizing the longitudinal movement of the intima-media complex (IMC). For the lateral M-mode, we set the target line in the longitudinal direction along the IMC and visualize the signals on the target line frame-by-frame by correcting the position of the target line along the radial displacement estimated by the phased tracking method. Differentiating the envelope signals between consecutive ultrasound beams was effective in visualizing the lateral movement of the IMC. The precision of the longitudinal displacement of the IMC estimated using the conventional block-matching method was validated by comparing it with the lateral M-mode. Because the conventional M-mode sequence plays an important role in evaluation of the dynamics of various tissues, the proposed "lateral M-mode" contributes to a detailed understanding of vascular dynamics and the development of diagnostic methods for vascular diseases.

The Methodology of Modified Frequency Band Envelope Kurtosis for Bearing Fault Diagnosis

Recently, the enhanced frequency band entropy (EFBE) was proposed based on the replacement of short-time Fourier transform with wavelet packet transform. In view of the shortcomings of EFBE, a feasible solution is provided, namely modified frequency band envelope kurtosis (MFBEK), which can be described as follows: First, the modified adaptive resonance bandwidth (MARB) based on the bearing inner-race fault frequency is proposed. The kurtosis of the envelope signal as an available indicator and the MARB are used to determine the optimal depth of MFBEK. Second, this special case, that is, the resonant frequency occurs at the junction of two adjacent subbands is considered, and a corresponding effective solution is provided to determine the optimal subband(s). Then, the reconstructed signal can be obtained. And then, if necessary, a band-pass filter is designed to process the reconstructed signal to enhance the noise reduction performance. Finally, envelope power spectrum analysis is performed on the reconstructed signal or the filtered signal to extract the fault characteristic frequency. In addition, a modified indicator is proposed to measure the analysis results. Analysis results on simulated and vibration signals measured from actual bearing have revealed that the MFBEK can obtain more robust performance.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

Damage detection in composite laminates using nonlinear guided wave mixing

This paper presents a delamination damage detection technique based on the nonlinear response of Lamb waves in composite laminates. In the approach, a network of transducers is employed to sequentially scan the structure by actuating and receiving dual guided waves. Using combined frequency waves originated due to contact nonlinearity when the incident dual frequency wave interacts with the damage, a damage localization imaging algorithm is proposed to locate the damage and determine the damage location. The proposed damage localization imaging algorithm is based on analysing the cross-correlation of the signal envelope at combined frequency wave with the excitation pulse envelope at incident waves for each transducer pair, the damage localization image is constructed. A numerical study using an experimentally validated finite element model is used to verify the proposed damage detection technique. The results of the numerical study show that, despite the small amplitude of the nonlinear components, the proposed method can effectively predict the damage location, and it does not require baseline measurements, which makes it potential to be a baseline-free damage detection technique.

Optimized weights spectrum autocorrelation: A new and promising method for fault characteristic frequency identification for rotating Machine fault diagnosis

Since fault characteristic frequencies (FCFs) and their harmonics are closely connected with specific fault types of rotating machines, identification of FCFs and their harmonics is a very crucial step for signal processing-based rotating machine fault diagnosis. Nowadays, Hilbert transform (HT) based square envelope spectrum (SES) and spectral coherence (SC) based SES are two main tools for FCF identification. Since the HT demodulates signals by calculating envelope signals in the time domain and the SC is based on the temporal instantaneous autocorrelation function of demodulated signals, the temporal waveform of a demodulated signal must be purified by using fault signature extraction methods, e.g., fast kurtogram, blind deconvolution, and their variants, etc. However, it has been extensively reported that these fault signature extraction methods are prone to be affected by interference components such as impulsive noise. Unlike the HT and SC which demodulate from the time domain, this paper aims to demodulate signals from the frequency domain to identify FCFs and their harmonics for rotating machine fault diagnosis. Firstly, it is demonstrated that Fourier spectrum autocorrelation is possible to indicate FCFs and their harmonics. Nevertheless, a troublesome problem is that interference spectral lines and noise spectral lines of real-world signals in the frequency domain will severely affect the performance of the Fourier spectrum autocorrelation. To solve such problem, this paper introduces a recently developed optimized weights spectrum (OWS) and innovatively designs an adaptive threshold method to respectively eliminate an influence of interference spectral lines and noise spectral lines. Thus, a new FCF identification method named optimized weights spectrum autocorrelation (OWSAC) is accordingly proposed. One main merit of the proposed OWSAC is that it does not need any fault signature extraction methods to do signal preprocessing. Two experimental case studies respectively on incipient bearing and gearbox diagnosis validate the effectiveness of the proposed OWSAC. The proposed OWSAC can achieve satisfactory performance respectively in two case studies and it is superior to five methods including HT-based SES, SC-based SES, optimized SES, fast kurtogram guided SES, and Fourier spectrum autocorrelation.

Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry.

The global navigation satellite system-interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal's "notch" and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil's attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.

AYARLANABİLİR RADYO FREKANS (RF) ALICI (RX) ENTEGRE DEVRE (IC)

In this work tunable radio frequency (RF) receiver (Rx) integrated circuit (IC) was demonstrated. TSMC 65 nm technology node was selected to implement IC. RF technique has some advantages over biomedical optic molecule investigation methods since it is easy to design, implement. Experimental measurement on live tissue is much easier than optic methods. Nanoscopic creatures have specific binding structures. Their elements construct their shapes and neighborhood conformation between their own atoms. Based on this work differential RF waves will be used to investigate the nanoscopic creatures. Since the investigation of nanoscopic creatures will require to use electromagnetic wave phase shift and scan of different RF frequencies, ultra-wide band (UWB) tunable receiver circuit topology was designed and simulated. For this purpose, Rx block was designed, and simulation work was demonstrated here. In the circuit simulations, off-chip antenna was connected to the low-noise amplifier (LNA) circuit. Specific frequency was around 30 GHz. Frequency tuning was adjusted by changing the source and bias voltages at the active inductor voltage-controlled oscillator (VCO) circuit block. The same VCO block was also used at the Tx circuit before. Antenna signal was modeled by using 33 GHz alternative sinusoidal signal. Antenna signal and 30 GHz active-inductor VCO voltage output were mixed at the mixer circuit block, 3 GHz envelope signal was extracted in this work. Layout implementation of Rx receiver IC was demonstrated.

Second-Order Statistics for IRS-Assisted Multiuser Vehicular Network With Co-Channel Interference

In this paper, we consider a downlink multi-user vehicular communication network empowered by an intelligent reflecting surface (IRS) with the base station (BS) employing the orthogonal frequency division multiple access (OFDMA) scheme. The performance of the considered system is evaluated in terms of second-order statistics (SOS) in the presence of multiple co-channel interferers with varying powers and speeds. In particular, we develop the analytical expressions of level crossing rate (LCR) and average outage duration (AOD) of the received signal envelope at a desired vehicle by considering a generalized fading model as - distribution. The impact of various system and channel parameters such as number of reflecting elements, amplitude of reflection coefficients, number of interferers, Doppler frequencies of desired and interfering vehicles, and different channel fading environments has been shown on the LCR and AOD performances. Moreover, we derive an expression for asymptotic LCR under high transmit power conditions. It has been shown that the asymptotic LCR is independent of the threshold and transmit power. In addition, we consider a stop and wait automatic repeat request (SW-ARQ) protocol for reliable data packet transmission and derive the expressions for packet error rate (PER) and throughput by utilizing the finite-state Markov channel (FSMC) model. The optimal packet length is also determined which maximizes the throughput under SW-ARQ protocol. The impact of various system parameters is also shown on the PER and throughput performances.

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

&lt;span lang="EN-US"&gt;This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.&lt;/span&gt;

Efficiency Analysis and Design Considerations of a Hysteretic Current Controlled Parallel Hybrid Envelope Tracking Power Supply

This paper presents the realization of an Envelope Tracking Power Supply (ETPS), for a sinusoidal envelope input signal. A parallel hybrid topology is chosen for the implementation. In this topology, a voltage-controlled Class-AB linear amplifier stage and a current-controlled switching converter stage operate in parallel. A hysteretic current control scheme is employed to control the operation of the switching converter. Block-level implementation of the ETPS is done using Simulink 2017b, continuous mode simulation. Simulations are performed using a sinewave input envelope signal = 1.94+ 1.2 sin(ω∙t). The input frequency varied from 1MHz to 60MHz. As the input frequency is increased, the ETPS moves from the linear to the non-linear region of operation. During the transition, the slew rates of the load current and the switching current match at a particular input frequency of 2MHz while the efficiency peaks. The maximum obtained efficiency while tracking the sinewave input signal is 82.3%. The way the efficiency can be optimized by focusing on the matching of the slew rates of load and switching currents is explained. Also, an insight into the study of various circuit parameters and the trade-offs that the designer needs to consider while designing an ETPS, is provided.

Expert System Based on Autoencoders for Detection of Broken Rotor Bars in Induction Motors Employing Start-Up and Steady-State Regimes

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions.

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.