Physical Magnitude Research Articles

Methodology for the Detection of Contamination and Gradual Outer Race Faults in Bearings by Fusion of Statistical Vibration–Current Features and SVM Classifier

Bearings are one of the main components of induction motors, machines widely employed in today’s industries, making their monitoring a primordial task; however, most systems focus on measuring one physical magnitude to detect one kind of fault at a time. This research tackles the combination of two common faults, grease contamination and outer race damage, as lubricant contamination significantly impacts the life of the bearing and the emergence of other defects; as a contribution, this paper proposes a methodology for the diagnosis of this combination of faults based on a proprietary data acquisition system measuring vibration and current signals, from which time domain statistical and fractal features are computed and then fused using LDA for dimensionality reduction, ending with an SVM model for classification, achieving 97.1% accuracy, correctly diagnosing the combination of the contamination with different severities of the outer race damage, improving the classification results achieved when using vibration and current signals individually by 7.8% and 27.2%, respectively.

Endogenous attention modulates automaticity of number processing.

Numerals (i.e., symbolic representations of numerical magnitude) are widespread in our environment and are fundamental to many decisions we make. It has been suggested that the processing of numerical magnitude is automatic. Various robust psychological effects, such as the distance effect (Moyer & Landauer, Nature, 215 (5109), 1519-1520, 1967) and the physical size-congruity effect (SiCE; Henik & Tzelgov, Memory & Cognition, 10 (4), 389-395, 1982), support this claim. Importantly, these effects demonstrate that the processing of numerical magnitude occurs unintentionally and while irrelevant to the task. These aspects often serve as criteria to assess the automatic nature of mental processes. However, evidence for the involvement of attention in the processing of magnitude of numerals somewhat subverts the automaticity account that was originally put forward. To reconcile between evidence in support of the automaticity account and evidence that compromises this account, we drew on another account of automaticity. This account distinguishes between strongly automatic and partly automatic mental processes based on their susceptibility to attentional influences. In the current study, we manipulated endogenous attention while participants completed numerical and physical comparisons of numerals, separately. We observed modulations of the SiCE for physical comparisons but not for numerical comparisons of numerals. That is, the processing of numerical magnitude when irrelevant was subjected to attentional influences, but the processing of their physical magnitude (i.e., size) was not. Therefore, we concluded that processing the numerical magnitude is partly automatic, whereas processing their physical magnitude is strongly automatic.

Supplemental Material for Perceiving Infinity: An Interplay Between Numerical and Physical Magnitude

Supplemental Material for Perceiving Infinity: An Interplay Between Numerical and Physical Magnitude

Physics symbolic learner for discovering ground‐motion models via NGA‐West2 database

AbstractGround‐motion model (GMM) is the basis of many earthquake engineering studies and practices. In this study, a novel physics‐informed symbolic learner (PISL) method is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to distill a concise and interpretable explicit characterization of complex systems of ground‐motions. In addition to the basic variables retrieved from previous GMMs, the current PISL incorporates three priori physical conditions, namely, distance, magnitude, and site amplification saturation. Based on the Next Generation Attenuation West2 database, GMMs developed using the PISL, an empirical regression method (ERM), and an artificial neural network (ANN) are compared in terms of residuals and extrapolation of peak ground acceleration and velocity. The results show that the inter‐ and intra‐event standard deviations of the three methods are similar. The functional form of the PISL is more concise than that of the ERM and ANN. The extrapolation capability of the PISL is more accurate than that of the ANN. The PISL‐GMM used in this study provide a new paradigm of regression that considers both physical‐ and data‐driven machine learning and can be used to identify the implied physical relationships and prediction equations of ground‐motion variables.

Exploring the Nexus of Domestic Violence: Impact on Married Women's Physical Health, Psychological Distress, and Magnitude of Abuse

Domestic violence is a common problem in Pakistan but due to stigma in surrounding it is not openly discussed. Current study determined the magnitude of domestic violence, physical health and psychological distress among females. Cross-sectional study was conducted in Sialkot among married females. Magnitude of domestic violence was considered as the determinant of physical health and psychological distress. Sample was comprised of 100 females from working and non-working background, selected through purposive sampling technique by random assignment. The variables were measured using three assessment tools composite abuse scale, Kessler’s psychological distress scale and physical health questionnaire. Data was analyzed by the application of SPSS, correlation and regression was measured for the assessment of purposed assumptions. Among married females domestic violence is the determinant of physical health (r=.702**, p < 0.01) and psychological distress (R= -.340*, p < 0.01).increase in the magnitude of domestic violence leads to the deterioration of physical health and decreases psychological distress.

Wavelet-Based Multiscale Intermittency Analysis: The Effect of Deformation.

Intermittency represents a certain form of heterogeneous behavior that has interest in diverse fields of application, particularly regarding the characterization of system dynamics and for risk assessment. Given its intrinsic location-scale-dependent nature, wavelets constitute a useful functional tool for technical analysis of intermittency. Deformation of the support may induce complex structural changes in a signal. In this paper, we study the effect of deformation on intermittency. Specifically, we analyze the interscale transfer of energy and its implications on different wavelet-based intermittency indicators, depending on whether the signal corresponds to a 'level'- or a 'flow'-type physical magnitude. Further, we evaluate the effect of deformation on the interscale distribution of energy in terms of generalized entropy and complexity measures. For illustration, various contrasting scenarios are considered based on simulation, as well as two segments corresponding to different regimes in a real seismic series before and after a significant earthquake.

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Optimizing Predictive Maintenance Decisions: Use of Non-Arbitrary Multi-Covariate Bands in a Novel Condition Assessment under a Machine Learning Approach

Jointing Condition-Based Maintenance (CBM) with the Proportional Hazards Model (PHM), asset-intensive industries often monitor vital covariates to predict failure rate, the reliability function, and maintenance decisions. This analysis requires defining the transition probabilities of asset conditions evolving among states over time. When only one covariate is assessed, the model’s parameters are commonly obtained from expert opinions to provide state bands directly. However, the challenge lies within multiple covariate problems, where arbitrary judgment can be difficult and debatable, since the composite measurement does not represent any physical magnitude. In addition, selecting covariates lacks procedures to prioritize the most relevant ones. Therefore, the present work aimed to determine multiple covariate bands for the transition probability matrix via supervised classification and unsupervised clustering. We used Machine Learning (ML) to strengthen the PHM model and to complement expert knowledge. This paper allows obtaining the number of covariate bands and the optimal limits of each one when dealing with predictive maintenance decisions. This novel proposal of an ML condition assessment is a robust alternative to the expert criterion to provide accurate results, increasing the expectation of the remaining useful life for critical assets. Finally, this research has built an enriched bridge between the decision areas of predictive maintenance and Data Science.

Event-triggered adaptive control of hypersonic vehicles subject to actuator nonlinearities

This paper investigates the event-triggered adaptive control problem for air-breathing hypersonic vehicles (AHVs) subject to physical actuator nonlinearities. In contrast with abundant AHV control strategies belonging to the traditional sample-data framework, the presented adaptive control works with an event-triggered mechanism, which naturally reduces the executing frequency of scramjet fuel-to-air equivalency ratio (FER) and aerodynamic control surface (ACS). During the event-triggered controller design, practical actuator characteristics including magnitude-rate composite constraints and servo transient dynamics are synthetically considered. In the velocity loop, the built-in FER saturation, which is a fusion of physical FER magnitude and rate constraints, is handled by reshaping the velocity reference with the help of an adaptive reference governor. Meanwhile in the altitude loop, a neural control allocator directly generates feasible ACS commands via a neural network that is well trained by means of the optimal control allocation. As the result, ACS magnitude-rate composite constraints and servo transient dynamics can be accommodated without implementing any time-consuming optimization searching process. Theoretical analysis indicates the guaranteed closed-loop stability and tracking performance while excluding the undesired Zeno behavior. Simulations validate the effectiveness of the proposed event-triggered adaptive control.

Thinking about order: a review of common processing of magnitude and learned orders in animals.

Rich behavioral and neurobiological evidence suggests cognitive and neural overlap in how quantitatively comparable dimensions such as quantity, time, and space are processed in humans and animals. While magnitude domains such as physical magnitude, time, and space represent information that can be quantitatively compared (4 "is half of" 8), they also represent information that can be organized ordinally (1→2→3→4). Recent evidence suggests that the common representations seen across physical magnitude, time, and space domains in humans may be due to their common ordinal features rather than their common quantitative features, as these common representations appear to extend beyond magnitude domains to include learned orders. In this review, we bring together separate lines of research on multiple ordinal domains including magnitude-based and learned orders in animals to explore the extent to which there is support for a common cognitive process underlying ordinal processing. Animals show similarities in performance patterns across natural quantitatively comparable ordered domains (physical magnitude, time, space, dominance) and learned orders (acquired through transitive inference or simultaneous chaining). Additionally, they show transfer and interference across tasks within and between ordinal domains that support the theory of a common ordinal representation across domains. This review provides some support for the development of a unified theory of ordinality and suggests areas for future research to better characterize the extent to which there are commonalities in cognitive processing of ordinal information generally.

Design of Biomedical Wireless Inclinometer Device and Comparison of Measurements with Image Processing Method

The slope difference between the two surfaces is a physical magnitude that needs to be measured in areas such as physical therapy diagnoses, construction, machinery, geology, geophysics, sports sciences, orthopedics. One of the devices used to measure the inclination difference is the wireless inclinometer. The wireless inclinometer allows you to find the slope of a surface relative to the ground. Inclinometers are widely used as a biomedical device, especially in the field of physical therapy. The wireless inclinometer devices used in this area are imported products and their prices are very high compared to their costs. In this study, an electronic device prototype was produced that can wirelessly measure the inclination difference of an object according to its gravity reference, show the measurement on the screen as an angle value, and record it. To verify the data obtained through the device, the calibration of the inclinometer is carried out with the inclinometer device with an accuracy of 0.001 degrees used in the Geomatics Engineering. In this study; by making measurements on the designed wooden human model, the measurements found by the image processing method were compared with the measurements of the wireless inclinometer device. The smallest angle difference between the angle measured by the device and the angle measured by the image processing method was found to be 0.013 degrees. In this study, it is aimed to develop a cost-effective domestic design and production device with superior features of the designed inclinometers.

Torres Quevedo's mechanical calculator for second-degree equations with complex coefficients

Leonardo Torres Quevedo worked intensively in analogue calculating machines during the last years of the 19th century. The algebraic calculators were calculating machines in which numbers are represented by quantities of a given physical magnitude(s). The physical result is a magnitude of a physical quantity whose measurement in the coherent unit is the result of the algebraic equation. This article shows the three-dimensional (3D) modelling, virtual reconstruction and simulation of the first mechanical calculating machine for solving second-degree equations with complex coefficients, to prove that the functionality was correct and the machine could be built. Sketches of said machine provide enough information on the shape and mechanisms of the machine. By means of the simulation, it has been possible to prove its operation and feasibility of construction so that it is possible to replicate it as a real physical model. The mechanical calculator for second-degree equations with complex coefficients constituted a major milestone in the technological development of the time and helped to originate and improve the design of other algebraic calculators like the machine for solving eighth-degree equations.

Comparative Diagnosis of the Urban Noise Problem from Infrastructural and Social Sensing Approaches: A Case Study in Ningbo, China.

Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks the concern of people’s perceived seriousness, especially at the macroscopic, i.e., citywide scale. In this paper, we borrow from the “exposure-perception-behavior” theory in environmental psychology, and propose an analytical framework for diagnosing the urban noise problem that integrates the Infrastructural and Social Sensing perspectives. Utilizing noise monitoring data that fills the spatiotemporal granularity gaps of official noise monitoring, as well as the “12345” urban complaint hotline records which serve as a proxy for residents’ perceived noise levels, we empirically examine the mechanisms for physical magnitude and perceived seriousness of urban noise, respectively, by taking the Jiangbei District of Ningbo City, China as an example. Results show that the existence of perceptual bias and behavioral preference effects did shape people’s perceived noise problem map that is vastly different from that of the physical noise magnitude, in which the semantics of urban places, temporal rhythms of life, and population demographics significantly influenced people’s tolerance of noise. We conclude the paper with suggestions on updating the existing National Standard for urban noise regulation to reflect the perceptual aspect, and also methodological discussions on possible ways to recognize and utilize the perceptual bias in social-sensing big-data to better accommodate urban governance.

Robust induced [formula omitted] optimal control of discrete-time systems having magnitude and rate-bounded actuators

The design of robust state- and output-feedback control for uncertain discrete-time systems with physical magnitude and rate constraints on their actuator dynamics was addressed. Unlike the traditional methods such as anti-windup (AW) methods, nested ellipsoids, model predictive controllers (MPCs) and integral quadratic constraints(IQCs) formulated by sector bounded inequalities, this paper uses a transformation of the system dynamics to a form which considers control signal and its rate as controlled outputs and using discrete-time ℓ∞ induced (peak-to-peak) norm from disturbance inputs to these outputs. To cope with the magnitude and rate bound non-linearities together, the induced ℓ∞ norm from disturbance input to the outputs involving control signal and its rate is utilised. On the other hand, discrete-time(DT) induced ℓ2 norm from disturbance input to the main controlled output is used to mitigate the effects of disturbances. We can tackle this ambitious non-linear control problem in the domain of linear convex multi-objective optimal control problem, which can be solved by effective semi-definite optimisation methods by using the proposed transformation and handling the control constraints in terms of worst case peak-to-peak gain of the system. Extended Linear Matrix Inequalities (LMIs) and full block S-procedure based design conditions developed over Linear Fractional Representation(LFR) framework allow the user to obtain robust state- and output-feedback control solutions with reduced conservatism. For the first time, this paper introduces an extended LMI based robust output-feedback control design for magnitude and rate bounded (MRB) systems, using full block S-procedure. We demonstrate the performance of the proposed controller through several simulations over benchmark examples covering systems having multi-variable structures and uncertainties. Our study also involves comparison results with a recently introduced technique based on multi-stage AW technique. The simulation results show that the proposed method of this paper is much effective and less conservative compared to the recent AW method provided in the literature.

On the Experimental Dynamic Force Performance of a Squeeze Film Damper Supplied Through a Check Valve and Sealed With O-Rings

Abstract In rotor-bearing systems, squeeze film dampers (SFDs) assist to reduce vibration amplitudes while traversing a critical speed and also offer a means to suppress rotor instabilities. Along with an elastic support element, SFDs are effective means to isolate a rotor from its casing. O-rings (ORs), piston rings (PRs), and side plates as end seals reduce leakage and air ingestion while amplifying the viscous damping in configurations with limited physical space. ORs also add a centering stiffness and damping to a SFD. The paper presents experiments to quantify the dynamic forced response of an O-rings sealed ends SFD (OR-SFD) lubricated with ISO VG2 oil supplied at a low pressure (0.7 bar(g)). The damper is 127 mm in diameter (D), short in axial length L = 0.2D, and the film clearance c = 0.279 mm. The lubricant flows into the film land through a mechanical check valve and exits through a single port. Upstream of the check valve, a large plenum filled with oil serves to attenuate dynamic pressure disturbances. Multiple sets of single-frequency dynamic loads, 10 Hz to 120 Hz, produce circular centered orbits with amplitudes r =0.1c, 0.15c, and 0.2c. The experimental results identify the test rig structure, ORs, and SFD force coefficients, namely, stiffness (K), mass (M), and viscous damping (C). The ORs' coefficients are frequency independent and show a sizeable direct stiffness, KOR ∼ 50% of the test rig structure stiffness, along with a quadrature stiffness, K0 ∼ 0.26KOR, demonstrative of material damping. The lubricated system damping coefficient equals CL = (CSFD + COR); the ORs contributing 10% to the total. The experimental SFD damping and inertia coefficients are large in physical magnitude; CSFD slightly grows with orbit size, whereas MSFD is relatively constant. The added mass (MSFD) is approximately fourfold the bearing cartridge (BC) mass; hence, the test rig natural frequency drops by ∼50% once lubricated. A computational physics model predicts force coefficients that are just 10% lower than those estimated from experiments. The amplitude of measured dynamic pressures upstream of the plenum increases with excitation frequency. Unsuspectedly, during dynamic load operation, the check valve did allow for lubricant backflow into the plenum. Post-tests verification demonstrates that, under static pressure conditions, the check valve does work since it allows fluid flow in just one direction.

Processing symbolic magnitude information conveyed by number words and by scalar adjectives.

Humans not only process and compare magnitude information such as size, duration, and number perceptually, but they also communicate about these properties using language. In this respect, a relevant class of lexical items are so-called scalar adjectives like “big,” “long,” “loud,” and so on which refer to magnitude information. It has been proposed that humans use an amodal and abstract representation format shared by different dimensions, called the generalised magnitude system (GMS). In this paper, we test the hypothesis that scalar adjectives are symbolic references to GMS representations, and, therefore, GMS gets involved in processing their meaning. Previously, a parallel hypothesis on the relation between number symbols and GMS representations has been tested with the size congruity paradigm. The results of these experiments showed interference between the processing of number symbols and the processing of physical (font-) size. In the first three experiments of the present study (total N = 150), we used the size congruity paradigm and the same/different task to look at the potential interaction between physical size magnitude and numerical magnitude expressed by number words. In the subsequent three experiments (total N = 149), we looked at a parallel potential interaction between physical size magnitude and scalar adjective meaning. In the size congruity paradigm, we observed interference between the processing of the numerical value of number words and the meaning of scalar adjectives, on the one hand, and physical (font-) size, on the other hand, when participants had to judge the number words or the adjectives (while ignoring physical size). No interference was obtained for the reverse situation, i.e., when participants judged the physical font size (while ignoring numerical value or meaning). The results of the same/different task for both number words and scalar adjectives strongly suggested that the interference that was observed in the size congruity paradigm was likely due to a response conflict at the decision stage of processing rather than due to the recruitment of GMS representations. Taken together, it can be concluded that the size congruity paradigm does not provide evidence in support the hypothesis that GMS representations are used in the processing of number words or scalar adjectives. Nonetheless, the hypothesis we put forward about scalar adjectives is still is a promising potential line of research. We make a number of suggestions for how this hypothesis can be explored in future studies.

A methodology for phenomenological analysis of cumulative damage processes. Application to fatigue and fracture phenomena

Sample functions, i.e., stochastic process realizations, are used to define cumulative damage phenomena which end into an observable terminal state or failure. The complexity inherent to such phenomena justifies the use of phenomenological models associated with the evolution of a physical magnitude feasible to be monitored during the test. Sample functions representing the damage evolution may be identified, once normalized to the interval [0,1], with cumulative distribution functions (cdfs), generally, of the generalized extreme value (GEV) family. Though usually only a fraction of the whole damage evolution, according to the specific problem handled, is available from the test record, the phenomenological models proposed allow the whole damage process to be recovered. In this way, down- and upwards extrapolations of the whole damage process beyond the scope of the experimental program are provided as a fundamental tool for failure prediction in the practical design. The proposed methodology is detailed and its utility and generality confirmed by its successive application to representative well-known problems in fatigue and fracture characterization. The excellent fittings, the physical interpretation of the model parameters and the good expectations to achieve a complete probabilistic analysis of these phenomena justify the interest of the proposed phenomenological approach with possible applications to other cumulative damage processes.

Density and Its Role in Predicting Interphase Characteristics and Dispersion in Poly(Lactic Acid) – Particulate Fillers Composites

Abstract Density measurements (i.e., physical magnitude) are quick, efficient, and crucial in determining the structure and quality of solid materials. For polymer composites, properties tend to increase, decrease, or both with density. In this work, poly(lactic acid) composites (PLAC) filled with carbon black (CB), multiwalled carbon nanotubes (MWCNTs), and cellulose nanofibers (CNFs) were prepared using the dissolution–dispersion method (DD) and oligomeric–dispersion method (OD). To study the hybrid effect, PLAC with 2.5 weight percent (wt%) CB + MWCNTs (0.2, 0.4, 0.6, 0.8, 1.0, 1.5 and 2.0wt%) and 2.5wt% CB + CNFs (0.2, 0.4, 0.6, 0.8, 1.0, 1.5 and 2.0wt%) were prepared using DD and OD. Density measurements were performed on PLAC to determine the extent of dispersion and interphase characteristics. PLAC prepared by OD showed an increase in density values in comparison to DD, irrespective of the filler.

Modeling of external self-excitation and force generation on magnetic nanoparticles inside vitreous cavity.

The purpose of this manuscript was to design a better method for recovery from rhegmatogenous retinal detachment (RRD) surgery. We attempted to achieve this by designing a helmet that can manipulate intraocular magnetic nanoparticles (MNPs) and create a magnetic tamponade, eliminating the need for postoperative head positioning. A simulated analysis was developed to predict the pattern of magnetic force applied to the magnetic nanoparticles by external magnetic field. No participants were involved in this study. Instead, magnetic flux and force data for three different helmet designs were collected using virtual simulation tools. A prototype helmet was then constructed and magnetic flux and force data were recorded and compared to virtual data. For both virtual and physical scenarios, magnitude and direction of the resulting forces were compared to determine which design created the controlled direction and strongest forces into the back of the eye. Of the three virtual designs, both designs containing a visor had greater force magnitude than magnet alone. Between both designs with visors, the visor with bends resulted in forces more directed at the back of the eye. The physical prototype helmet shared similar measurements to virtual simulation with minimal percent error (Average = 5.47%, Standard deviation = 0.03). Of the three designs, the visor with bends generated stronger forces directed at the back of the eye, which is most appropriate for creating a tamponade on the retina. We believe that this design has shown promising capability for manipulating intraocular MNPs for the purpose of creating a tamponade for RRD.

Implementation of a cooling system to develop experimental fatigue tests below zero grades Celsius

Temperature is one of the matter properties with the greatest influence on the materials behavior existing in nature and those man designed. This physical magnitude allows to demonstrate and define behaviors and materials characteristics in the industry in general. Its influence is present in all the places where the materials fulfill functions, however, in some applications its influence is very little, which allows to disregard its effects. In the steels case (one of the most used materials at the industrial level today), the temperature variation produces dilation or contraction, depending on the temperature magnitude and its variation that affects them. Temperature increases generate an expansion phenomenon in the materials, which under load will reach a point where they present thermal fatigue failure. The opposite is that of temperature drops, where the phenomenon that occurs is contraction, often leading to the loss of adjustments and interference that compromise the equipment functionality and integrity. The fatigue failure mentioned is presented as a result of the stresses and deformations present in both cases. It is desired to condition a rotational flex fatigue test equipment with a cooling system to bring the sample material to temperatures below zero degrees Celsius, in order to check how low temperatures, affect the resistance of steel to fatigue. For this, a cooling system was designed and the fatigue equipment was adapted to reduce heat transfer. After carrying out this implementation and determining that the sizes of the devices were suitable for the proposed purposes, the initial temperature tests were carried out and, once this part was achieved, three repetitions of a rotary fatigue test were performed that demonstrated that the equipment can operate normally.