Different Stages Of Fatigue Research Articles

Magnetic Barkhausen Noise Technique for Fatigue Detection and Classification in Martensitic Stainless-Steel

Abstract Stainless steel is used in many applications because of its excellent mechanical properties at elevated temperatures. Material fatigue is a major problem in steel structures and can cause catastrophic damage resulting in significant economic consequences. Conventional nondestructive evaluation techniques can detect macro defects but do not perform well when it comes to material degradation due to fatigue, which happens at a microstructure level. It is well known that stress applied on a material will have an impact on the microstructure and produces a change in the magnetic properties of the material. Hence, magnetic nondestructive evaluation techniques that are sensitive to changes in magnetic properties play a major role in the early-stage fatigue detection, i.e., before the macro crack initiates. This paper introduces the magnetic Barkhausen noise technique to garner information about fatigue state of the material under test. K-medoids clustering algorithm and genetic optimization algorithm are used to classify the stainless-samples into fatigue categories. The results prove that martensitic grade stainless-steel samples in different stages of fatigue can be classified into broad fatigue categories, i.e., low fatigue, mid fatigue, and high fatigue based on the remaining useful life of the sample.

Nonlinear Eddy Current Technique for Fatigue Detection and Classification in Martensitic Stainless-Steel Samples

ABSTRACT The increasing use of stainless steel in industrial structures can be attributed to its excellent mechanical properties at elevated temperatures. Martensitic grade stainless-steel is used, for example, to manufacture steam turbine blades in power plants. The failure of these turbine blades can result in equipment damage contributing to expensive plant failures and safety concerns. Degradation and structural failure of these blades is largely attributed to material fatigue, at the microstructure level. Hence, it is important to evaluate the level of fatigue prior to the initiation of macro defects to ensure the viability of these components. Conventional nondestructive evaluation (NDE) techniques such as ultrasonic testing and eddy current testing are suitable in detection of macro defects such as cracks, but not very effective in evaluating degradation of the material at a microstructure scale. This article investigates the feasibility of the nonlinear eddy current (NLEC) technique to detect fatigue in martensitic grade stainless-steel samples along with a methodology to classify the samples. K-medoids clustering algorithm and genetic algorithm are used to classify the samples according to the severity of fatigue. Initial results indicate that stainless-steel samples, in different stages of fatigue, can be classified into broad categories of low, mid, and high levels of fatigue.

Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection

Fatigue may cause a decrease in mental and physical performance capacity, which is a serious safety risk for the drivers in the transportation system. Recently, various studies have demonstrated the deviations of electroencephalogram (EEG) indicators from normal vigilant state during fatigue in time and frequency domains. However, when considering spatial information, these feature descriptors are not satisfying the demand for reliable detection due to the well-known challenge of signal mixing. In this paper, we propose a novel approach based on clustering on brain networks (CBNs) to alleviate the problem to improve the performance of driver fatigue detection. The clustering algorithm was employed to extract the spatial nodes with distinct connectivity attributes throughout the EEG-based brain networks. Then, the temporal features of wavelet entropy from the extracted nodes were transformed to spatio-temporal images so that the image edge detection method (pulse-coupled neural networks) to distinguish different stages of fatigue can be used. The experimental results demonstrated the temporal features from the extracted nodes reduced signal mixing and showed clearer deviations. The detected fatigue based on the imaging method was to an extent consistent with self-reported subjective feelings and most of the critical fatigue was detected before the subjective feelings of fatigue. For all the subjects, 21 of 29 accidents happened after detected fatigue in the simulated driving task. Therefore, the proposed method owns potential value for early warning and avoidance of traffic accidents caused by driver fatigue.

Fatigue damage evaluation of carbon fiber reinforced composites using nonlinear resonance spectroscopy

The present work explores the use of nonlinear resonance spectroscopy to detect early fatigue damage in carbon fiber-epoxy composite laminates. The objective is to test composite samples at mid to high cycle fatigue (>10∧3 cycles), so that the damage evolution could be tracked. The nondestructive nature of the testing method ensures that the sample can be loaded and tested till failure, i.e. interrupted fatigue testing. The acoustic nonlinear response tracked over the fatigue life of the sample includes, the nonlinear frequency shift and modal damping ratio. Additionally, slow dynamic diagnostics was also carried out to investigate the recovery rate and recovery time. The samples that were tested were all quasi-isotropic laminates under 4-point bending load. These included a pristine sample, and two samples with impact damage to introduce variability. Tracking the nonlinear parameters over fatigue life showed a non-monotonic increase in nonlinearity. Conjectures based on acoustic response to microdamage and different stages of fatigue were used to explain the observed variation.

Estimation of remaining useful life of fatigued plate specimens using Lamb wave‐based nonlinearity parameters

The present study focuses on estimating material nonlinearity and consequently remaining useful life of fatigued specimens using the amplitude- and physics-based material nonlinearity parameters evaluated for Lamb wave motion in plate specimens. The amplitude-based nonlinearity parameter depends on amplitudes of the Lamb wave harmonics generated due to material nonlinearity. Here, it is employed to estimate the inherent and dislocation induced material nonlinearities for different stages of fatigue. The cumulative effect is obtained from the strict matching of phase and group velocities of the S1 − S2 mode pair. The physics-based nonlinearity parameter is obtained from the higher order elastic coefficients, plastic coefficients, and substructural evolution parameters. It does not depend on wave propagation distance; however, it depends on percent fatigue life. Thus, it is used to construct a theoretical nonlinearity curve. A spectral amplitude normalization technique is given to systematically evaluate the material nonlinearity, once the Lamb wave data over different wave propagation distances are known either from experiments or from simulations. The values of amplitude-based nonlinearity parameter thus estimated through the simulation and experiments for different fatigue stages are then plotted onto the obtained theoretical nonlinearity curve. A reasonably good agreement is seen between the fatigue life estimations given by both the nonlinearity parameters. Thus, the amplitude-based nonlinearity parameter obtained from the Lamb wave response can be effectively used to estimate the remaining useful life of the fatigued plate specimens.

Is Fatigue-induced Loss Of Power Associated With a Change In The Curvature Of The Force-velocity Relation?

In skeletal muscles, the ability to generate power is reduced during fatigue. Maximal power (Pmax) is determined by the force-velocity relationship, which can be closely approximated by the Hill equation, containing three key parameters: 1) Maximal isometric force (Fmax) 2) Maximum contraction velocity (Vmax) and 3) Force-velocity curvature. PURPOSE: To investigate the possible association between the fatigue-induced loss of power and changes in curvature of the force-velocity relation in muscles with different fiber type composition. METHODS: The force-velocity relationship was measured before and during development of fatigue in isolated rat soleus (slow-twitch) and EDL (fast-twitch). Muscles were incubated in Krebs-Ringer solution at 30 C° and stimulated electrically at 60 Hz for 0.75 s (soleus) or at 150 Hz for 0.2 s (EDL) to obtain serial concentric contractions leading to fatigue. The Force-velocity relationship was measured at different stages of fatigue and after 1 hour of recovery. Force-velocity data were fitted to the Hill equation, and curvature was determined as the ratio of the curve parameters a/F0 (inversely related to curvature). RESULTS: At the end of the fatiguing protocol, maximal power had decreased by 58 ± 5% (soleus) and 69 ± 4% (EDL) compared to initial values in non-fatigued muscles. Curvature increased in both muscle types as judged from the decrease in a/F0 of 81 ± 20% (soleus) and by 31 ± 12% (EDL). At the end of the fatiguing protocol, the calculated contributions to the total loss of Pmax was 25 ± 10% from Fmax, 17 ± 3% from Vmax and 58 ± 14% from a/F0 (soleus) and 64 ± 6% from Fmax, 16 ± 3% from Vmax and 20 ± 9% from a/F0 (EDL). Complete recovery of a/F0 was observed following one hour of rest in both muscle types. CONCLUSIONS: Increased curvature of the force-velocity relationship occurs during fatigue in both slow and fast twitch muscles. Particularly in slow-twitch muscles, this increase in curvature is strongly associated with fatigue-induced loss of power.

Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals

In this paper, the focus is on the analysis of a scalp electroencephalography (EEG) database of human subjects using the electrophysiological source imaging or source localization and the classification of normal and sleep-deprived states. The EEG collection was carried out while the subjects were driving in simulated condition in a laboratory, where the fatigue level propagates through 11 different stages of fatigue, to achieve the sleep deprivation of a total period of 36 h. Standardized low-resolution brain electromagnetic tomography (sLORETA) algorithm has been used here for estimating the source activations on the surface of the neo-cortex. sLORETA transforms the surface or scalp EEG data to the corresponding corticular dipole sources at each voxel on a simulated neo-cortex. For the characterization of the underlying neural patterns, approximate and sample entropies in voxels nearest to specific electrodes for different subjects and varying fatigue levels have been computed. Approximate entropy, sample entropy, and modified sample entropy are used here as the measures of complexity, similarity, and regularity in the sources. As a further investigation, these measures computed over all the stages are used to train a support vector machine, which classifies the measured values between alert and extremely fatigued states. As a result, several observations on the nature of change of the chaotic entropies are provided, and up to 86% classification accuracy is obtained.

Influence of temperature on the average velocity of muscle fatigue in Rhinella marina Sartorius

To analyze and compare the effect of temperature on the average velocity of the linear phase and the exponential phase stages produced by periodic stimulation with simple twitching or with tetanus in the toad sartorius. Methods: an in vitro experimental study with a sample of 46 toad sartorius muscles randomly selected. At the temperatures studied, peak tension produced with two stimulus patterns (twitching and tetanus) were measured until reaching the corresponding muscle fatigue in each case. The tension velocity drop in the linear phase and the exponential phase for each type of fatigue were calculated, and the regression slopes obtained with the Arrhenius equation were compared. Results: The temperatures used (1 to 12°C) significantly affected the velocity of fatigue in the stages of linear and exponential phases of both types of fatigue (p 0.05). Conclusions: The temperature significantly affected average development velocity of fatigue in the different phases of the two types of fatigue, but when comparing slopes of most regressions corresponding to Arrhenius there were no significant differences, suggesting that the mechanisms underlying the different stages of fatigue have equal sensitivity to temperature.

Acoustic Emission Assessment of Impending Fracture in a Cyclically Loading Structural Steel

Using the advanced acoustic emission (AE) technique, we address the problem of early identification of crack initiation and growth in ductile structural steels under cyclic loading. The notched 9MnSi5 steel specimens with weld joints were fatigue tested at room and lower temperatures with concurrent AE measurements. Detection of AE in ductile materials where fatigue crack initiation and propagation is mediated by local dislocation behavior ahead of the notch or crack tip is challenging because of an extremely low amplitude of the AE signal. With account of this issue, two new practically oriented criteria for recognition of different stages of fatigue are proposed on the basis of AE data: (1) a power spectrum-based criterion and (2) a pattern recognition-based criterion utilizing modern clustering algorithms. The applicability of both criteria is verified using obtained AE data. A good correspondence between AE outcomes and experimental observations of the fatigue behavior was obtained and is discussed.

Quantifying Muscle Fatigue of the Low Back during Repetitive Load Lifting Using Lyapunov Analysis

Background: Occupational low back disorders are often associated with exposure to work-related physical risk factors such as muscle fatigue in the low back. Objective: The objective of this study was to investigate the possible relationship between the divergence of the kinematic trajectories of the low back system and the different stages of fatigue during the execution of a repetitive lifting task. Methods: The patterns of the low back system were recorded using markers on specific vertebras during the repetitive load lifting from the floor to a 0.75 m height table. The maximum Lyapunov exponent, λmax of the recorded patterns was calculated from the x and y coordinates of the lower back markers using the algorithm proposed by Wolf. Results: The results of the λmax values determined three different sections of muscle fatigue which were also in agreement with the Borg’s clinical scale of perceived fatigue results. The assessment of the λmax values between the three different sections showed a descriptive point where the muscle fatigue accumulation may have resulted in a change of the low back control. Conclusion: Lyapunov exponent methodology could be a reliable methodology for ergonomists to provide an index to design the work/rest ratio ergonomically.

Fatigue damage evaluation by metal magnetic memory testing

Tension-compression fatigue test was performed on 0.45% C steel specimens. Normal and tangential components of magnetic memory testing signals, H p(y) and H p(x) signals, with their characteristics, K of H p(y) and H p(x)M of H p(x), throughout the fatigue process were presented and analyzed. Abnormal peaks of H p(y) and peak of H p(x) reversed after loading; H p(y) curves rotated clockwise and H p(x) curves elevated significantly with the increase of fatigue cycle number at the first a few fatigue cycles, both H p(y) and H p(x) curves were stable after that, the amplitude of abnormal peaks of H p(y) and peak value of H p(x) increased more quickly after fatigue crack initiation. Abnormal peaks of H p(y) and peak of H p(x) at the notch reversed again after failure. The characteristics were found to exhibit consistent tendency in the whole fatigue life and behave differently in different stages of fatigue. In initial and crack developing stages, the characteristics increased significantly due to dislocations increase and crack propagation, respectively. In stable stage, the characteristics remained constant as a result of dislocation blocking, K value ranged from 20 to 30 A/(m·mm)−1, and H p(x)M ranged from 270 to 300 A/m under the test parameters in this work. After failure, both abnormal peaks of H p(y) and peak of H p(x) reversed, K value was 133 A/(m·mm)−1 and H p(x)M was −640 A/m. The results indicate that the characteristics of H p(y) and H p(x) signals were related to the accumulation of fatigue, so it is feasible and applicable to monitor fatigue damage of ferromagnetic components using metal magnetic memory testing (MMMT).

Multisource domain adaptation and its application to early detection of fatigue

We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this article, we propose two transfer learning frameworks based on the multisource domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed frameworks, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the first framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects) and the key feature of the second framework is a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multisource domain adaptation formulation using the weighted Rademacher complexity measure. We have validated the proposed frameworks on Surface ElectroMyoGram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG dataset demonstrate that the proposed method improves the classification accuracy by 20% to 30% over the cases without any domain adaptation method and by 13% to 30% over existing state-of-the-art domain adaptation methods.

EEG signal analysis for the assessment and quantification of driver’s fatigue

Fatigue in human drivers is a serious cause of road accidents. Hence, it is important to devise methods to detect and quantify the fatigue. This paper presents a method based on a class of entropy measures on the recorded Electroencephalogram (EEG) signals of human subjects for relative quantification of fatigue during driving. These entropy values have been evaluated in the wavelet domain and have been validated using standard subjective measures. Five types of entropies i.e. Shannon’s entropy, Rényi entropy of order 2 and 3, Tsallis wavelet entropy and Generalized Escort-Tsallis entropy, have been considered as possible indicators of fatigue. These entropies along with alpha band relative energy and (α + β)/δ1 relative energy ratio have been used to develop a method for estimation of unknown fatigue level. Experiments have been designed to test the subjects under simulated driving and actual driving. The EEG signals have been recorded along with subjective assessment of their fatigue levels through standard questionnaire during these experiments. The signal analysis steps involve preprocessing, artifact removal, entropy calculation and validation against the subjective assessment. The results show definite patterns of these entropies during different stages of fatigue.

Effect of an alternating magnetic field on the working capacity of human muscles under the conditions of local or general fatigue

The effect of a magnetic field on changes in working capacity at different stages of fatigue during cyclic exercises was studied. The data obtained indicated that the magnetic field affected the neuromuscular system, increasing working capacity under the conditions of considerable local or general fatigue without causing pathological changes in neuromuscular regulation.

Evolution of Rayleigh constant in fatigued lead zirconate titanate capacitors

The capacitance of Pt/PbZr0.5Ti0.5O3/Pt capacitors versus the strength of applied ac electric field E has been measured for samples that fatigued at different square pulse switching cycles. Based on the in-series capacitor model, the variation of interface capacitance and bulk ferroelectric capacitance at different stages of fatigue were treated separately. A simplified method was derived to calculate the initial dielectric constant and Rayleigh constant for the bulk ferroelectric film from the ac electric field dependence of the sample capacitance. It was found that the initial dielectric constant and Rayleigh constant for the bulk ferroelectric film changed a little when the number of cycles is less than 3×106, whereas it decreased remarkably when the number of cycles is greater than 3×106. The suppression of polarization was attributed to pinning of the domain walls at the electrode/ferroelectric interface.

Spectral analysis of acoustic emission during cyclic deformation of copper single crystals

Abstract Acoustic emission (AE) is investigated during fatigue at constant-plasticstrain control of copper single crystals oriented for easy glide, aiming at AE monitoring of structural transformation and dislocation self-organization during cyclic deformation. The results of AE analysis are compared with the cyclic response and hysteresis loop shape on different stages of fatigue. It is shown that the modern AE technique employing spectral analysis is sensitive to all essential structural changes in dislocation ensembles during fatigue such as matrix hardening, formation of veins and channels, persistent slip band (PSB) nucleation and development until crack initiation. The evidence is given for early identification of strain localization by means of AE. It is shown that the maximum of median frequency of the AE power spectral density at the intermediate stage of rapid hardening can be considered as an indicator of local softening followed by strain localization in PSBs. The moment of crack nucleation is...

Spectral analysis of acoustic emission during cyclic deformation of copper single crystals

Abstract Acoustic emission (AE) is investigated during fatigue at constant-plasticstrain control of copper single crystals oriented for easy glide, aiming at AE monitoring of structural transformation and dislocation self-organization during cyclic deformation. The results of AE analysis are compared with the cyclic response and hysteresis loop shape on different stages of fatigue. It is shown that the modern AE technique employing spectral analysis is sensitive to all essential structural changes in dislocation ensembles during fatigue such as matrix hardening, formation of veins and channels, persistent slip band (PSB) nucleation and development until crack initiation. The evidence is given for early identification of strain localization by means of AE. It is shown that the maximum of median frequency of the AE power spectral density at the intermediate stage of rapid hardening can be considered as an indicator of local softening followed by strain localization in PSBs. The moment of crack nucleation is...

Effects of hydrostatic pressure on fatiguing frog muscle fibres.

Effects of increased hydrostatic pressure (range 0.1-10 MPa) on isometric twitch and tetanic contractions of single, intact, frog muscle fibres were examined at 4, 11 and 21 degrees C and at different stages of fatigue. Twitch tension was potentiated by pressure at all temperatures, but the extent of potentiation was more pronounced at higher temperatures (34% MPa-1 at 21 degrees C, compared to 8% MPa-1 at 4 degrees C). Tetanic tension was depressed by pressure at 4 degrees C (approximately 0.7% MPa-1) but was potentiated by pressure at 21 degrees C (approximately 0.4% MPa-1). The effect of hydrostatic pressure on the tetanic tension was dependent on the fatigue status of the muscle fibre: during the early stages of fatigue (when tetanic tension was depressed by < 20%), high pressure produced a tension depression (as in an unfatigued muscle fibre), whilst during the later stages of fatigue high pressure induced a significant potentiation of tetanic. Our results support the suggestion that excitation-contraction coupling and contractile activation are impaired during late fatigue. Pressure-effects were basically similar to caffeine-effects under a variety of conditions, suggesting that an enhancement of Ca2+ release may be contributory to potentiation of twitch tension and, in severely, fatigued muscle, potentiation of tetanic tension. In the rested state and during early fatigue the main effect of pressure is an inhibition of the crossbridge cycle.

TWO KINDS OF DISLOCATION PATTERNS AND THE FORMING REASONS DURING THE PUSH-PULL FATIGUE PROCESS FOR Al SINGLE CRYSTALS

Variations of the stress σm and internal friction Ｑ－１ versus cyclic number N under push-pull fatigue strain εt=６×１０－4 for [100] Al single crystals were measured. The dislocation configuration at different stages of fatigue was observed by TEM. A elucidation was given with the view of dislocation slip geometry.

The relation between the variation of stress, energy loss, ultrasonic attenuation, and dislocation configuration in aluminium during the early stages of fatigue

Push-pull fatigue deformation of 99.999 Al polycrystals is performed at room temperature at strain amplitudes in the range (25 to 800) × 10−6. Variations of the stress σ, energy loss ΔE, and ultrasonic attenuation Δα versus cycle number N under various push-pull fatigue strain amplitude ea are measured systematically, and the relative dislocation configuration at different stages of fatigue is observed by TEM. The mechanism of the variation of σ, ΔE, and Δα during fatigue cycling is analysed by correlating the fatigue experimental results with the TEM observation on the dislocation configuration.