Analysis Of Acceleration Signals Research Articles

Experimental Determination of Influences of Static Eccentricities on the Structural Dynamic Behavior of a Permanent Magnet Synchronous Machine

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively.

Frailty detection in older adults via fractal analysis of acceleration signals from wrist-worn sensors

PurposeFrailty is a reversible multidimensional syndrome that puts older people at a high risk of adverse health outcomes. It has been proposed to emerge from the dysregulation of the complex system dynamics of physiologic control systems. We propose the analysis of the fractal complexity of hand movements as a new method to detect frailty in older adults.MethodsFRAIL scale and Fried’s phenotype scores were calculated for 1209 subjects—72.4 (5.2) y.o. 569 women—and 1279 subjects—72.6 (5.3) y.o. 604 women—in the pubicly available NHANES 2011–2014 data set, respectively. The fractal complexity of their hand movements was assessed with a detrended fluctuation analysis (DFA) of their accelerometry records and a logistic regression model for frailty detection was fit.ResultsGoodness-of-fit to a power law was excellent (R2>0.98\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{2}>0.98$$\\end{document}). The association between complexity loss and frailty level was significant, Kruskal–Wallis test (df = 2, Chisq = 27.545, p-value <0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.001$$\\end{document}). The AUC of the logistic classifier was moderate (AUC with complexity = 0.69 vs. AUC without complexity = 0.67).ConclusionFrailty can be characterized in this data set with the Fried phenotype. Non-dominant hand movements in free-living conditions are fractal processes regardless of age or frailty level and its complexity can be quantified with the exponent of a power law. Higher levels of complexity loss are associated with higher levels of frailty. This association is not strong enough to justify the use of complexity loss after adjusting for sex, age, and multimorbidity.

Reverse flow in a pressure suppression system due to condensation instabilities

The Vacuum Vessel Pressure Suppression System (VVPSS) is designed to protect the ITER reactor (Vacuum Vessel, VV) from an overpressurization accident. The VVPSS is mainly composed of four Vapour Suppression Tanks (VSTs) partially filled with water inside which venting pipes (spargers) are immersed to direct the steam in the subcooled pool to condense it. Since the maximum pressure allowed inside the VV is only 0.15 MPa, a sub-atmospheric pressure must be maintained inside the VSTs. During a series of experimental campaigns funded by ITER Organization to study and validate the correct operability of the VVPSS and conducted in a large-scale plant built at the B. Guerrini laboratory of the University of Pisa, pressure instability and reverse flow through the sparger holes have been observed. The analysis of acceleration signals recorded during this pressure inversion, showed large amplitude oscillations at low frequency due to the Condensation Induced Water Hammer (CIWH).

Investigating the Combined Effect of Multiple Dent and Bump Faults on the Vibrational Behavior of Ball Bearings

The rolling element bearing is a fundamental component of any rotating machinery. During operation, wear debris and lubricant impurities create dents and bumps on the bearing raceway surfaces. Such localized defects produce transient vibration impulses at one of the bearing characteristic frequencies. Having a combination of multiple types of point defects on the raceway results in superimposed vibration patterns, which reduce the ability to recognize these defects’ effects. In this paper, a 6-DOF dynamic model is developed to accurately investigate the vibration characteristic of a ball bearing with a multipoint defect comprising a dent and bump on its raceway surface. The model considers the effects of time-varying contact force produced due to defects, lubricant film damping, bearing preload, and the inertia effect of rolling elements. The simulation results reveal the vibration behavior of multipoint defect bearings. In addition, bearing vibration response is affected by the number of defects, the angle between them, and the type and size of each defect. Furthermore, it is challenging to predict bearing defects parameters such as the numbers, types, sizes, and angles between adjacent defects from acceleration signal analysis without jerk signal analysis. The validation of the model is proved using signals from the Case Western University test setup.

Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry.

This study aimed to use the k-nearest neighbor (kNN) algorithm, which combines gait stability and symmetry derived from a normalized cross-correlation (NCC) analysis of acceleration signals from the bilateral ankles of older adults, to assess fall risk. Fifteen non-fallers and 12 recurrent fallers without clinically significant musculoskeletal and neurological diseases participated in the study. Sex, body mass index, previous falls, and the results of the 10 m walking test (10 MWT) were recorded. The acceleration of the five gait cycles from the midsection of each 10 MWT was used to calculate the unilateral NCC coefficients for gait stability and bilateral NCC coefficients for gait symmetry, and then kNN was applied for classifying non-fallers and recurrent fallers. The duration of the 10 MWT was longer among recurrent fallers than it was among non-fallers (p < 0.05). Since the gait signals were acquired from tri-axial accelerometry, the kNN F1 scores with the x-axis components were 92% for non-fallers and 89% for recurrent fallers, and the root sum of squares (RSS) of the signals was 95% for non-fallers and 94% for recurrent fallers. The kNN classification on gait stability and symmetry revealed good accuracy in terms of distinguishing non-fallers and recurrent fallers. Specifically, it was concluded that the RSS-based NCC coefficients can serve as effective gait features to assess the risk of falls.

Online gear hobbing error estimation based on shaft vibration signal analysis

This paper presents a method for online estimation of hobbing error based on the workpiece shaft vibration analysis. The hobbing vibration originates from the intermittent cutting process and directly influences the machining accuracy of gears. By analyzing the vibration data of workpiece shaft, the gear error estimation is completed. Firstly, mathematical models, combining tooth geometric characteristics with vibration displacements, for evaluating helical gear profile deviation, pitch deviation, and helix deviation are established. Secondly, an orthogonal experiment of gear machining is carried out and the vibration acceleration data of workpiece shaft are collected during processing. Finally, based on the vibration displacements obtained by frequency domain integration of acceleration data, the gear errors are estimated. The comparison between the estimation errors and the actual errors proves that the estimation model is effective. In addition, by analyzing the orthogonal experiment results and factors, and the spectrum analysis of acceleration signals, the order of the influence degree of machining parameters and the vibration characteristics under different parameters were obtained. The research results can provide theoretical and practical references for error compensation and parameter optimization of gear machining.

Dynamic Characteristics of Gear Coupling and Rotor System in Transmission Process Considering Misalignment and Tooth Contact Analysis

The dynamic vibration of the gear coupling-rotor system (GCRS) caused by misalignment is an important factor of low frequency vibration and noise radiation of the naval marine. The axial misalignment of gear coupling is inevitable owing to mass eccentricity, and is unconstrained in axial direction at high-speed operation. Therefore, the dynamic model of GCRS is proposed, considering gear-coupling misalignment and contact force in this paper. The whole motion differential equation of GCRS is established based on the finite element method. Moreover, the numerical calculation method of meshing force, considering the uniform distribution load on contact surface, is presented, and the mathematical predictive time–frequency characteristics are analyzed by the Newmark stepwise integral approach. Finally, a reduced-scale application of the propulsion shaft system is utilized to validate the effectiveness of the proposed dynamic model. For the sensibility to low-frequency vibration, the natural frequencies and vibration modes of GCRS are analyzed through the processing and analysis of acceleration signal. The experimental dynamic response and main components of vibration are respectively consistent with mathematical results, which demonstrate the effectiveness of the proposed dynamic model of GCRS with misalignment. Furthermore, it also shows that the proposed finite element analysis and calculation method are suitable for complex shafting, providing a novel thought for dynamic analysis of the propeller–shaft–hull coupled system of marine.

Dynamic Modeling and Vibration Characteristics Analysis of Transmission Process for Dual-Motor Coupling Drive System

The dual-motor coupling drive system (DCDS), which is widely used in electric vehicles, has attracted increasing attention due to its high transmission efficiency and economical fuel consumption. Current research has mainly focused on the control scheme of dual motors and has ignored the dynamic characteristics of the asymmetrical transmission structure. This paper presents an investigation of a dynamic model and an analysis method of the transmission process for the DCDS. The entire dynamic model of the DCDS was established by considering the planetary gear, differential bevel gear, and drive shaft with the transfer matrix method (TMM). Then, a detailed theoretical analysis was developed to study the influence of meshing stiffness and excitation source on the dynamic characteristics. Finally, the DCDS experimental platform was utilized to validate the effectiveness of the proposed dynamic model. For susceptibility to low-frequency vibrations, the first four natural frequencies and vibration modes of the DCDS were analyzed through the processing and analysis of acceleration signals. The experimental dynamic responses were generally consistent with the numerically computed results, which demonstrates the effectiveness of the proposed dynamic model with TMM. Furthermore, the proposed dynamic analysis method may be helpful for developing effective control algorithms to suppress vibrations and achieving highly smooth motions for electric vehicles.

Seismic acceleration signal analysis and application

Although various types of geophones are applied in seismic exploration, there are only three common types of signals produced by geophones: displacement, velocity, and acceleration signals. Currently, our understanding of the signal characteristics, such as the generation mechanism, the geophysical properties, and the significance of the corresponding rock physics, remains unclear, which makes it difficult to both scientifically evaluate and take full advantage of the different types of geophones. In this paper, the mechanism by which seismic waves are generated is studied based on the spring-damped vibration theory. The physical characteristics of the three above-mentioned signal types and the relationships among the physical properties of the signals and medium are analyzed, as well as the signal-to-noise ratio (SNR), resolution, and spectrum characteristics. Based on laboratory tests, field experiments, and applications, we obtained the following conclusions. The acceleration signal reflects the elastic characteristics of the medium and the change rules, and the signal strength is positively correlated with physical property changes. The acceleration signal has favorable attributes, such as small distortion, high fidelity, strong high-frequency amplitudes, and a wide frequency band. Therefore, the acceleration signal is more suitable for high-precision seismic exploration of complex media. In addition, the P-wave acceleration signal more accurately reflects the elastic Young modulus, shear modulus, and density changes than the velocity signal. However, the sensitivity decreases with increasing shear modulus and density. For the S-wave, the acceleration signal is more sensitive to the shear modulus and density than the velocity signal.

Resonance speed measurement of high-speed spindle using an instruction-domain-based approach

Although the manufacturing and assembling technology of the machine tool has become more and more mature, eccentricity of the spindle is still unavoidable and is an important source of spindle vibration. When the spindle rotation frequency is close or equal to the natural frequency of the spindle structure, spindle vibration will increase sharply, causing resonance (resonance speed). The high-speed spindle is more prone to cause resonance speed due to its wide speed range and large amount of centrifugal force. Machining at the resonance speed not only degrades the surface quality but also accelerates the degradation of the spindle performance. Aiming at this problem, this paper presents a G instruction-domain-based resonance speed measurement method. This method realizes the measurement by performing the spindle acceleration signal analysis in the instruction domain, which overcomes the shortcomings of the hammer impact test-based method (complicated operation), it is suitable for in situ application. The effectiveness of the proposed method was verified by hammering modal test and motor vibration test. The influence of resonance speed on the milling surface quality and the influence of different tools on the resonance speed was explored. Based on the proposed method, a portable spindle resonance speed measurement tool and a CNC system resonance speed avoidance module were developed.

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

Condition monitoring has been an active area of research in many industrial fields during the last decades, particularly in fluid power systems. This paper presents a solution for the fault diagnosis of a variable displacement axial-piston pump, which is a critical component in many hydraulic systems. The proposed methodology follows a data-driven approach including data acquisition and feature extraction and is based on the analysis of acceleration signals through the theory of cyclostationarity. An experimental campaign was carried out on a laboratory test bench with the pump in the flawless state and in faulty states. Different operating conditions were considered and each test was repeated several times in order to acquire a suitable population to verify data repeatability. Results showed the capability of the proposed approach of detecting a typical fault related to worn slippers. Future works will include tests in order to apply the approach to a wider set of faults and the development of a classifier for accurate fault identification.

Development of a LabVIEW Application for Measurement and Analysis of Acceleration Signals from an External Reference

Development of a LabVIEW Application for Measurement and Analysis of Acceleration Signals from an External Reference

Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Continuous Wavelet Transform Analysis of Acceleration Signals Measured from a Wave Buoy

Accelerometers, which can be installed inside a floating platform on the sea, are among the most commonly used sensors for operational ocean wave measurements. To examine the non-stationary features of ocean waves, this study was conducted to derive a wavelet spectrum of ocean waves and to synthesize sea surface elevations from vertical acceleration signals of a wave buoy through the continuous wavelet transform theory. The short-time wave features can be revealed by simultaneously examining the wavelet spectrum and the synthetic sea surface elevations. The in situ wave signals were applied to verify the practicality of the wavelet-based algorithm. We confirm that the spectral leakage and the noise at very-low-frequency bins influenced the accuracies of the estimated wavelet spectrum and the synthetic sea surface elevations. The appropriate thresholds of these two factors were explored. To study the short-time wave features from the wave records, the acceleration signals recorded from an accelerometer inside a discus wave buoy are analysed. The results from the wavelet spectrum show the evidence of short-time nonlinear wave events. Our study also reveals that more surface profiles with higher vertical asymmetry can be found from short-time nonlinear wave with stronger harmonic spectral peak. Finally, we conclude that the algorithms of continuous wavelet transform are practical for revealing the short-time wave features of the buoy acceleration signals.

A method for evaluating the vibrational response of racing bicycles wheels under road roughness excitation

The aim of the work was the development of a method for measuring and comparing the vibrational response of different racing rear wheels to the excitation caused by riding on irregular road surfaces. Four different rear wheels were selected for the study. Vertical accelerations at rear wheel axis and at the seatpost were measured during field tests performed while cruising on different road surfaces at different constant speeds. Frequency analysis of acceleration signals was performed using random signal analysis methods. The results show that the ranking between comfort properties of different wheels varies with the road surface roughness and the cruising speed considered.

Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint

Arthritis is one of the leading causes of disability and affects a major segment of the population. Consequently, accurate diagnosis of arthritis is important. Arthritis due to calcium pyrophosphate deposition disease (CPPD), rheumatoid arthritis, and spondyloarthropathy, induce complex changes in the cartilage and the articular surface. The fractal dimension provides a measure of the complexity of a signal. Recently, we have developed non-invasive acceleration measurements to characterize the arthritic patients. The question remains if the fractal dimension of the acceleration signal is different for different arthritis conditions. The purpose of this study was to distinguish between different types of arthritis of the finger joint using the fractal dimension of the acceleration signal obtained from the finger joint of the arthritic patients. Acceleration signals were obtained from the finger joint of arthritis patients with rheumatoid arthritis, spondyloarthropathy, and calcium pyrophosphate deposition disease of the finger joint. ANOVA results showed that there were significant differences between the fractal dimension of acceleration signals from patients having calcium pyrophosphate deposition disease and rheumatoid arthritis and spondyloarthropathy. Fractal dimension of acceleration signals, in concert with other clinical symptoms, can be used to classify different types of arthritis.

Analysis of trotter gait on the track by accelerometry and image analysis.

The aim of this study was to describe the correlation between the phases of the limb cycle of trotters on the track and specific points on the acceleration curves obtained from a new gait analysis system. We compared kinematic data obtained by video image analysis and 3-dimensional acceleration recordings made on 3 French trotters in training. They trotted on a race track at speeds of 8.33, 10 and 11.66 m/s, with a final stretch at maximum speed. Their locomotion was recorded with a synchronised video camera at a frame frequency of 200 Hz and with the Equimétrix gait analysis system. The gait variables were calculated using 3-dimension acceleration data recorded at the sternum (dorso-ventral, longitudinal and lateral axes) at a sampling rate of 100 Hz. Three phases of the stride were clearly identified on the dorsoventral acceleration signal: hoof-landing, midstance phase and toe-off. Braking and propulsion phases were identified on the corresponding longitudinal acceleration signal. The weight-bearing diagonal was identified by observing the lateral signal. The stride temporal variables (stride, stance, braking and propulsion durations for both diagonals), measured by video analysis and by acceleration signal analysis, were not significantly different (P>0.05). The identification of specific points on the acceleration pattern allowed an accurate temporal analysis of the stride. Potential applications could be the determination of locomotor factors related to racing performance or assessment of locomotor disorders at high speed.