RMS Signal Research Articles

Lean Blowout Proximity Sensing in a Low Emission Liquid-Fueled Combustor

ABSTRACT Advanced aircraft engine combustor designs, intended for achieving low NOx emissions, necessitate lean combustion with small operating margins above the LBO boundary, posing a high risk of exhibiting lean blowout (LBO) problems. Employing active LBO controllers receiving input from LBO proximity sensors can allow for narrower margins, thereby yielding multiple performance gains. Although several previous studies investigated LBO proximity sensing, they predominantly focused on premixed gas-fueled combustors. The application of proximity sensing approaches to liquid-fueled combustor designs, relevant to aircraft engines have not received adequate attention. Therefore, this study investigates LBO proximity sensing in a low emission lean direct injection (LDI) liquid-fueled combustor design developed by NASA. The combustor operates at atmospheric pressure with preheated air. The proximity sensing is examined by analyzing both OH* and CH* chemiluminescence signals, with a focus on assessing their relative performances. The signal analysis approaches include spectral, partial extinction event (LBO precursor) detection, signal RMS, and the CH*/OH* ratio, with an emphasis on the first two methods. Both steady-state and transient operating conditions have been examined. The combustor did not exhibit large-scale flame partial extinction and re-ignition events near LBO, as commonly observed in gas-fueled combustors. However, an increase in flame unsteadiness and partial extinction events with very subtle signatures in optical signals, have been observed. Low-pass filtering has been observed to considerably enhance the precursor signatures enabling their improved detection. With a reduction in LBO margin, an increase in signal low-frequency power, RMS, and precursor event occurrence rate have been observed, which were utilized for providing LBO proximity measures. A key finding from this study is that CH* signals provide significantly superior performance compared to OH* for the proximity sensing in all the approaches, and the potential reasons are discussed.

Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals

Surface electromyography techniques are widely used in field of motion analysis and ergonomics combining precise muscular activation assessment with low-invasiveness and wearability. The aim of this investigation is to identify the myoelectrical manifestations of fatigue and to compare the effectiveness of sEMG-based quantitative indices for fatigue assessment. The investigated indexes are the ARV and RMS signal amplitudes, the mean frequency, the median frequency, the Dimitrov index, the instantaneous mean frequency and Wavelet distribution-based WIRE51 index. Two different protocols were developed, and the activity of the lateral deltoid and middle trapezius muscles was recorded. The WIRE51 index is found to have the highest sensitivity in the detection of the difference between the repetitions of each exercise for both protocols. Due to the lack of a unified standard for the performance comparison of fatigue indices, a correlation analysis was carried out between the result provided by the indices and the subjective fatigue perception employing the RPE scale.

Hybrid methodology development for lubrication regimes identification based on measurements, simulation, and data clustering

The lubrication regimes of a laboratory scale journal bearing were analysed with wide band acoustic emission (AE) measurements. The analysis was supported by data-based clustering of AE data. Digital twin of the journal bearing was generated with Simpack multi-body simulation software to study opportunities for developing a hybrid methodology for more complex systems monitoring. The AE and data-based clustering approach can be effectively used to reveal fundamental lubrication modes, i.e., hydrodynamic (HL), mixed (ML) and boundary (BL) lubrication as a function of Hersey number, which can be evaluated in situ by utilizing digital twin. Besides AE the other parameters monitored were friction torque, bearing temperature, loading, sliding velocity and oil pressure. The materials used in the experiments were case-hardened 18CrNiMo7–6 steel and nitrided 42CrMo7 steel. The tests were lubricated with synthetic extreme-pressure gear oil (SGN 320) and the bearing temperature was kept constant during the tests. The bearing loading and sliding velocity during tests were varied in the wide range resulting in different lubrication situations. The acoustic emission signals power and frequency content was analysed, and essential features were extracted for data clustering. For lubrication regime change identification the parameters such as signal RMS and coefficient of variation (CV) proved to be important, while signal kurtosis showed to be the most sensitive in discovering anomalies. The high sensitivity requires data filtering to remove erroneous peaks and to reveal real trends more clearly. It is also interesting to notice the changes in AE frequency during changing to different lubrication regime. In literature different clustering and classification methods has been proposed and applied for journal bearing status identification. Here the selected unsupervised clustering method was the mean-shift clustering due to fact, that the lubrication regimes in the Stribeck curve form an inseparable continuum. The algorithm does not require specifying the number of clusters in advance, i.e., the clusters are determined by the algorithm with respect to the data. The results were compared to simulations with digital twin. i.e., by comparing simulated digital twin film thickness and measured AE kurtosis relative to measured Hersey number. It was concluded that digital twins can be utilized as virtual sensor for in situ detecting of lubrication regimes, if it is possible to calibrate the simulation with sensitive measurements, e.g., AE. It is obvious that simulations alone cannot predict suddenly appearing anomalies, such as impurities or surface fatigue failures.

Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.

INDIRECT DRILL CONDITION MONITORING BASED ON MACHINE TOOL CONTROL SYSTEM DATA

Automatic process monitoring, including tool wear monitoring, is a key aspect of improving the energy efficiency and cost of the machining process. The tool flank wear continuously increases during drilling operations. The intensity of tool wear may vary depending on the local properties of the material and the process settings. This paper shows the potential of drill condition monitoring based on machine tool control system data, namely spindle drive current and Z slide current. The workpiece vibration measurement is used as a reference method. Correlations of various features of monitored signals are evaluated. These features are shown to depend, in general, on the instantaneous drilling depth. Among the features investigated, the RMS signal has been shown to exhibit a significant correlation with tool wear. The results were compared for two values of cutting speed. The correlation of selected features is shown to be independent of the total lifetime of the tool, thus demonstrating the attractiveness of these features for tool wear prediction. Specifically, the root mean square of the vibration and spindle torque signals strongly correlate with flank wear near the bottom of the hole while the root mean square of the drive current of the drilling axis strongly correlates with flank wear near the middle of the hole.

Day–night cycle of seismic noise HVSR and comparison with body waves andTwaves

SUMMARYWe analysed long (months) continuous recordings of ground motion at more than 30 sites in Calabria (Italy) in order to investigate the relationships among background signal amplitude, noise composition, day–night cycle and horizontal to vertical spectral ratio (HVSR). We computed the root mean square (rms), polarization and HVSR of the seismic signal. For many sites, the HVSR contains at least one well-defined peak of amplitude greater than 2 that is representative of site effects produced by the shallow geological structure and/or topography. At six of the investigated sites, we observe an important variation of the HVSR peak amplitude that is well correlated with the day–night cycle, the peak amplitude being greater during day hours, when the background signal amplitude is higher. The rectilinearity of particle motion computed from the polarization analysis is higher during day hours, thus showing a positive correlation with both signal rms and HVSR peak amplitude. For these sites we analysed also body waves of local earthquakes and T waves produced by regional earthquakes in order to compute the HVSR of signals composed predominantly by body waves. Results of body waves and T waves are more similar to the HVSR of day hour seismic noise than the HVSR of night hour seismic noise, thus suggesting that the stronger seismic noise recorded during day hour may contain a greater amount of body waves with respect to the night hour noise.

Cardiac Arrhythmia Classification based on the RMS Signal and Cyclostationarity

The goal of this paper is to present a method of classification for cardiac arrhythmias. This method is based on the analysis of the (RMS) signal extracted from the electrocardiogram (ECG) leads and the heart beat cyclostationarity nature. The coefficients of spectral correlation fuction are useful in order to differentiate among the five types of beats: Normal (N), Premature Ventricular Contraction (PVC), Premature Atrial Contraction (APC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). Some simple statistical measures for spectral correlation coefficients are extracted while Principal Component Analysis (PCA) is used to reduce its features. Finally, the classification using Artificial Neural Networks (ANNs) is implemented. Two experiments were performed, one that used a multi-lead ECG signal, and the other that used the RMS signal as input. These experiments showed that when using the RMS signal better results are obtained. This last method led to some specific results: sensitivity, specificity and accuracy indexes of 97.68%, 99.08% and 98.72%, respectively.

Machinability metallic materials estimation based on acoustic emission of turning

An experimental study of the possibility of using the acoustic emission signal during turning to assess the machinability of steel by cutting has been carried out. According to the results of previous studies, it has been shown that the use of the total acoustic energy of the cutting process to assess the machinability does not have sufficient resolution. The article presents the results of measurements of the acoustic emission signal in comparison with the results of measuring the tool wear during resistance tests. A group of samples of six steels with a known chemical composition and mechanical properties were used as materials. The group includes steels that differ a priori in machinability and steels whose machinability cannot be predicted in advance. Endurance tests were carried out for face turning with a tool made of highspeed steel with a variable cutting speed. The maximum height of the wear area in the direction perpendicular to the cutting edge was used as a criterion for machinability. The acquisition of the acoustic emission signal was carried out by a broadband sensor built into the tool holder with a preamplifier of 60 dB and a bandwidth of 50...1000 kHz. The integral energy characteristic of the RMS signal is used as a criterion for predicting the processability. As a result of the experiments, it was found that the use of an acoustic emission signal has a good resolution in assessing the machinability by turning steels.

The difference of surface electromyography data processing method based on simulated manal-lifting-task

Objective: To study the differences of different signal processing method of surface electromyography (sEMG) in judging muscle fatigue. Methods: From July to October 2019, based on the model of simulated manual lifting operation, the original sEMG signals from 13 volunteers of brachial radial muscle, brachial two-headed muscle, triangle muscle, left vertical spine muscle, right vertical spine muscle and lateral femoral muscle were collected in the operation activities. Three different electromyography signal processing methods (all signal from motion beginning to the end, peak signal and ehe specified motion signal) were used to analyze the original data in time domain (RMS) and frequency domain (MDF) , the data difference between different electromyography signal processing methods was analyzed by using Wilcoxon rank and sum test and nonlinear curve fitting method. Results: The age of the subjects of the simulated lifting operation was (24.31±2.02) years old, height (173.78±4.84) cm, weight (66.28±5.58) kg, body mass index (BMI) 21.94±1.58. The thickness of triceps skinfold was (14.08±4.86) mm, and the thickness of the skin fold under the scapula was (15.54±3.59) mm. After processing the original signal data by using different sEMG signal interception methods, the normality test, Levene's test, and the Wilcoxon test showed that, except for the MDF index of the brachial two-headed muscle, the differences in the RMS and MDF signals of the other muscles were statistically significant (P<0.016) . The all signal processing method dealed with data distribution dispersion better than other methods, and the rate of change of RMS signal slope was higher than other methods. Non-linear regression results showed that all signal processing method had low volatility in processing data, and the regression equation had a high degree of fit. Conclusion: Different electromyography signal processing methods have differences. The all signal processing method which intercepts from starting point to the end point of action cycle has the least data volatility, and electromyography time domain and frequency domain index with the highest sensitivity of time, which is suitable for the application of surface electromyography to judge muscle fatigue in dynamic and complex operations.

The Influence Of Normalization Technique On Between-muscle Activation During A Back-squat: Methodological Considerations

BACKGROUND/OBJECTIVE: Currently, no gold standard electromyography (EMG) normalizing technique exists when conducting between-muscle comparisons of muscle activity during isotonic resistance training exercises. The aim of this study was to assess if between-muscle activation during the back-squat differed among electromyography (EMG) normalization techniques when normalizing to: (1) 1 repetition maximum (1RM), (2) maximal voluntary isometric contraction (MVIC), and (3) the first of a set of three repetitions (Rep1%) in trained female lifters. METHODS: Thirteen participants completed a back-squat 1RM, MVIC of the rectus-femoris (RF) and gluteus-maximus (GM), and three repetitions of the back-squat at 80% 1RM. For the 1RM and MVIC normalization techniques, the average of the peak RMS signal of both muscles during the three submaximal reps were normalized to the peak 1RM and MVIC signals. The Rep1% averaged the peak RMS signals of both muscles during the 2nd and 3rd submaximal repetitions normalized to the peak signal during the 1st repetition. RESULTS: The RF-GM between-muscle EMG (ΔEMG) differed among normalization techniques (p < 0.001, ηp2 = 0.48). Post-hoc pairwise comparisons indicated MVIC normalization elicited different ΔEMG with large effects compared to both 1RM (p = 0.037; d = 1.2) and Rep1% (p = 0.004; d = 1.9) techniques, but the 1RM and Rep1% did not produce different ΔEMG (p = 0.27; d = 0.8). CONCLUSION: Our findings suggest EMG normalization technique influences the magnitude and direction of between-muscle activation during common lifting exercises, and we recommend normalizing isotonic movements to dynamic normalization methods such as a 1RM or Rep1%. Key Words: Electromyography; Methodology; Signal processing; Lower extremity; Training; Exercise

Buzz-based recognition of the honeybee colony circadian rhythm

Honeybees are one of the highly valued pollinators. Their work as individuals is appreciated for crops pollination and honey production. It is believed that work of an entire bee colony is intense and almost continuous. The goal of the work presented in this paper is identification of bees circadian rhythm with a use of sound-based analysis. In our research as a source of information on bee colony we use their buzz that have been analysed using algorithms. For the purpose of bees day/night definition, a dedicated electronic system has been developed. The data analysis involves demonstration of the circadian rhythm based on the RMS signal level. Method for defining the start and end of the presumed bees’ night was also presented. Mel Frequency Cepstral Coefficients (MFCCs) features and SVM classifier were used. The performed experiment shown the existence of repetitive cycles, which may indicate the presence of bee night. An attempt was made to estimate the time range of this phenomenon.

Failure analysis in single-cylinder diesel engine SK-MDF300 through acoustic emissions

In the present study, the diagnosis of a single-cylinder diesel engine is evaluated through acoustic emissions. The acoustic signal from four engine locations, which include the engine block, the injection system, the fuel tank, and the fuel pump, is analyzed using the medium quadratic root technique. The analysis is carried out at a fixed speed of 3200 rpm and a load percentage of 20%, 40%, and 80%. The results obtained show that the comparison of the acoustic signals of two different time periods allows to quickly and easily evaluate changes in the engine conditions. The sensitivity of acoustic emissions makes it possible to identify different engine load conditions. The study of the acoustic signal in different engine locations allowed to identify the injection system as one of the main areas prone to fail or changes in its operating condition. Additionally, the analysis method based on the acoustic signal RMS allowed to clearly identify faults in the injection system. The above could be done by comparing signals and analyzing the variability of the signal. Therefore, the ability of acoustic emissions to perform a rapid and low-cost diagnosis is demonstrated, without requiring relevant physical modifications to the engine.

Tool life estimation using acoustic emission signal

A study was carried out on predicting the working life of a metal-cutting tool using an acoustic emission signal in comparison with predicting the working life of a metal-cutting tool based on the standard chemical and mechanical properties of the material being processed. A group of samples of six steels with a known chemical composition and mechanical properties was used as materials. Lifetime testing was carried out during milling with a single-blade cutter and the initial ranking of the steels according to their workability based on the relative intensity of wear rate of the tool was obtained. Acoustic emission signal reading was performed under similar processing conditions with determination of the integral energy characteristic of the RMS signal. During comparison of the relative hardness, the relative sulfur content and the relative RMS, it was found out that only for RMS there is correct compliance with the results of lifetime testing. The result obtained shows the effectiveness of the use of the acoustic emission signal for predicting the tool working life.

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001–−0.4611), and negligible to moderate positive correlations with ST (r = 0.004–0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters.

Gear misalignment diagnosis using statistical features of vibration and airborne sound spectrums

Failure in gears, transmission shafts and drivetrains is very critical in machineries such as aircrafts and helicopters. Real time condition monitoring of these components, using predictive maintenance techniques is hence a proactive task. For effective power transmission and maximum service life, gears are required to remain in prefect alignment but this task is just beyond the bounds of possibility. These components are flexible, thus even if perfect alignment is achieved, random dynamic forces can cause shafts to bend causing gear misalignments. This paper investigates the change in energy levels and statistical parameters including Kurtosis and Skewness of gear mesh vibration and airborne sound signals when subjected to lateral and angular shaft misalignments. Novel regression models are proposed after validation that can be used to predict the degree and type of shaft misalignment, provided the relative change in signal RMS from an aligned condition to any misaligned condition is known.

High spectral resolution second harmonic generation microspectroscopy at thin layer interfaces with broadband continuum pulses

We demonstrate an effective microspectroscopy technique by tracing the dispersion of second order nonlinear optical susceptibility χ(2) in single atomic layer materials. The experimental method relies on the detection of single-shot second harmonic (SH) spectra from the materials and the subsequent data normalization. The key point in our study is that we used a broadband (˜350 nm) near-infrared femtosecond continuum pulses generated at high repetition rates in a photonic crystal fiber with superior spatial quality and stable spectral power density. This is opposite to the point-by-point laser tuning method in SH generation spectroscopy that was applied extensively in the past and has shown limited precision in obtaining χ(2) dispersion. The continuum pulse technique produces spectral resolution better than 2 meV (<0.3 nm at 450 nm) and shows low (<5–6% rms) signal detection noise allowing the detection of subtle features in the χ(2) spectrum at room temperatures. Fine sub-structure features within the main peak of χ(2) spectra indicate the impact of broadened resonances due to exciton transitions in the single layer materials.• Tailored continuum pulses are used to generate second harmonic signal in non-centrosymmetric semiconductors.• SHG spectrum carries fingerprints of the bandstructure around the direct gap states.• The technique produces fine spectral resolution and much better signal-to-noise ratio compared to point-by-point wavelength tuning methods.

Digital Current Measurement Element for Operation During Current Transformer Severe Saturation

The development and implementation of a digital current measurement element for proper operation during current transformer (CT) magnetic core severe saturation are considered. CT transient performance is often accompanied by primary current transformation to secondary one with great errors. In this case the secondary CT current which is an input signal of the digital measurement element differs from the ideally transformed CT current both in shape and magnitude. This causes impermissible signal settling time at the standard digital measurement element output. As a result, main requirements to the protection devices such as reliability and fast operation are violated, that in some cases makes the high-speed protection device ineffective. To solve this problem, it is proposed to form the output signal of the digital current measurement element in depending on the input signal total harmonic distortion (THD) coefficient value. Moreover, it is worthwhile to form the output signal so that for a low CT saturation conditions this output signal slightly differs from the secondary current RMS value, and for a severe CT saturation conditions it exceeds this value. Digital current measurement element model has been developed and implemented in the MatLab-Simulink environment using the following blocks: a digital filter block responsible for the input signal fundamental frequency component magnitude calculation; a digital filter block responsible for the input signal RMS value calculation; and, also, a standard blocks for basic mathematical calculations needed for proper functioning of the proposed measurement element. The functional testing of the proposed digital current measurement element model was carried out using the signal, that was similar in form to the waveform of the secondary current of the severe saturated CT. The tests that had been performed confirmed that the proposed digital current measurement element in comparison with the standard current measurement element ensures stable functioning and enhanced operation time during transients.

An online bunch length and momentum spread measurement method based on multiple BPMs

Bunch length and momentum spread are two crucial parameters in proton and ion linacs. A non-destructive model-independent rms bunch length and momentum spread measurement method that uses button BPMs is presented in this paper. By using the exponential relation between BPM signal amplitude and rms bunch length, as well as the linear relation between rms bunch length and drift time, rms bunch length and rms momentum spread are derived from several BPMs (three at least) located along the beam line. The effects of beam displacement, beam size, and BPM quantity on the measurement are analyzed. The rms bunch length and the rms momentum spread of the 3-MeV proton beam at the Compact Pulsed Hadron Source (CPHS) of Tsinghua University were successfully measured using this method. The measured rms momentum spread 0.54% is consistent with the simulation value. In addition, the resolution for the rms bunch length and the rms momentum spread measurements were ±0.017 ns and ±0.016%, respectively.

Microgrid Islanding Detection Based on Mathematical Morphology

Voltage and frequency stability are highly important for reliable performance of smart grids. In grid-connected mode, the utility controls these parameters, but when islanding occurs these parameters exceed their limits, which may result in irreparable damage to the system. This paper presents a time-domain approach which uses basic mathematical morphology (MM) operators, dilation and erosion filters, for microgrid islanding detection. The proposed method applies a dilation-erosion differential filter (DED) of the RMS signal (DEDFOR) at the point of common coupling (PCC) in a micro-grid connected to distributed generations (DGs). To evaluate the performance of the proposed approach, it is tested and compared with existing techniques in the literature under various conditions such as capacitor bank switching and motor starting. The results verify the accuracy and efficiency of the proposed technique for islanding detection under different operating conditions and various power mismatches.

Analysis of the Possibility of Using Acoustic Emission for Monitoring Technical Condition of the Fuel Injector in 3AL25/30 Engine

Abstract Acoustic emission method (AE) can be used for the diagnosis of machine parts such as, for example: fuel injectors. This paper presents the methodology and research results of 3AL25/30 engine fuel injector. During research was studied one injector in good condition and second with simulated failure involving closing 2 of 9 holes of the injector tip. Research was carried out on a laboratory test stand using a set of acoustic emission Vallen System. This set included: 4 channel signal recorder AMSY 6, two measurement modules ASIP-2/S, preamplifier with a frequency range 20 kHz-1 MHz and the strengthening of 34 dB, AE signal measurement sensor type VS 150M, with a frequency range 100-450 kHz. During the study, the acoustic emission (AE) generated by tested injector was recorded. The following parameters were determined: amplitude, rise time, duration time, total time, number of events – hits, the effective value of the signal (RMS). Analysis of the results showed significantly longer total time of the injection in the case of damaged injector compared to the injector in good conditions. Signal amplitude was higher, however, the RMS signal reached approximately 3-times lower value for the injector with damaged tip. This means lower quality fuel atomization. Laboratory test results were compared with signals recorded on injectors installed in the engine. Analysis of the signals allowed detection damage of the injector installed in the engine during normal operation.