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In order to improve the design of the actuators of a Dragon Fly prototype, we study the loads applied to the actuators in operation. Both external and inertial forces are taken into account, as well as internal loads, for the purposes of evaluating the influence of the compliance of the arms on that of the "end-effector". We have shown many inadequacies of the arms regarding the stiffness needed to meet the initial design requirements. In order to reduce these inadequacies, a careful structural analysis of the stiffness of the actuators is carried out with a FEM technique, aimed at identifying the design methodology necessary to identify the mechanical elements of the arms to be stiffened. As an example, the design of the actuators is presented, with the aim of proposing an indirect calibration strategy. We have shown that the performances of the Dragon Fly prototype can be improved by developing and including in the control system a suitable module to compensate the incoming errors. By implementing our model in some practical simulations, with a maximum load on the actuators, and internal stresses, we have shown the efficiency of our model by collected experimental data. A FEM analysis is carried out on each actuator to identify the critical elements to be stiffened, and a calibration strategy is used to evaluate and compensate the expected kinematic errors due to gravity and external loads. The obtained results are used to assess the size of the actuators. The sensitivity analysis on the effects of global compliance within the structure enables us to identify and stiffen the critical elements (typically the extremities of the actuators). The worst loading conditions have been evaluated, by considering the internal loads in the critical points of the machine structure results in enabling us the sizing of the actuators. So that the Dragon fly prototype project has been set up, and the first optimal design of the arms has been performed by means of FEM analysis.

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy.

Roller bearing degradation features fractal characteristics such as self-similarity and long-range dependence (LRD). However, the existing remaining useful life (RUL) prediction models are memoryless or short-range dependent. To this end, we propose a RUL prediction model based on fractional Brownian motion (FBM). Bearing faults can happen in different places, and thus their degradation features are difficult to extract accurately. Through variational mode decomposition (VMD), the original degradation feature is decomposed into several components of different frequencies. The monotonicity, robustness and trends of the different components are calculated. The frequency component with the best metric values is selected as the training data. In this way, the performance of the prediction model is hugely improved. The unknown parameters in the degradation model are estimated by the maximum likelihood algorithm. The Monte Carlo method is applied to predict the RUL. A case study of a bearing is presented and the prediction performance is evaluated using multiple indicators.

Nonlinear partial differential equations emerge in an extensive variants of physical problems inclusive of fluid dynamics, solid mechanics, plasma physics, quantum field theory as well as mathematics and engineering. It has also been noticed that systems of nonlinear partial differential equations arise in biological and chemical applications. This article presents the analytical investigation of a completely generalized (3 + 1)-dimensional nonlinear potential Yu-Toda-Sasa-Fukuyama equation which has applications in the fields of engineering and physics. The generalized version of the potential Yu-Toda-Sasa-Fukuyama equation is more comprehensively studied in this paper compared to other research work previously done on the equation, with various new solutions of interests achieved. The theory of Lie group is applied to the nonlinear partial differential equation to basically reduce the equation to an integrable form which consequently allows for direct integration of the result. The rigorous process involved in performing a comprehensive reduction of the model under consideration using its Lie algebra makes it possible to achieve various nontrivial solutions. Besides, more general solutions are found via a well-known standard technique. In consequence, we secured diverse solitons and solutions of great interest including topological kink solitons, singular solitons, algebraic functions, exponential function, rational function, Weierstrass function, Jacobi elliptic function as well as series solutions of the underlying equation. Moreover, the completeness of the result was ascertained by presenting the solutions graphically. In addition, discussions of the pictorial representations of the results are done. Conclusively, we constructed conserved quantities of the underlying equation via both the variational and non-variational approaches using the classical Noether’s theorem as well as the standard multiplier technique respectively. In addition, some pertinent observations made from the secured results via both techniques are analyzed.

Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we propose fractional Lévy stable motion for degradation modeling. First, we define fractional Lévy stable motion simulation algorithms. Then, we demonstrate the LRD and heavy-tailed property of fLsm to provide support for the model. The proposed method is validated with the C-MAPSS dataset obtained from the turbofan engine. Principle components analysis (PCA) is conducted to extract sources of variance. Experimental data show that the predictive model based on fLsm with exponential drift exhibits superior accuracy relative to the existing methods.

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

Tool wear will reduce workpieces’ quality and accuracy. In this paper, the vibration signals of the milling process were analyzed, and it was found that historical fluctuations still have an impact on the existing state. First of all, the linear fractional alpha-stable motion (LFSM) was investigated, along with a differential iterative model with it as the noise term is constructed according to the fractional-order Ito formula; the general solution of this model is derived by semimartingale approximation. After that, for the chaotic features of the vibration signal, the time-frequency domain characteristics were extracted using principal component analysis (PCA), and the relationship between the variation of the generalized Hurst exponent and tool wear was established using multifractal detrended fluctuation analysis (MDFA). Then, the maximum prediction length was obtained by the maximum Lyapunov exponent (MLE), which allows for analysis of the vibration signal. Finally, tool condition diagnosis was carried out by the evolving connectionist system (ECoS). The results show that the LFSM iterative model with semimartingale approximation combined with PCA and MDFA are effective for the prediction of vibration trends and tool condition. Further, the monitoring of tool condition using ECoS is also effective.

Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.

Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.

Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on-site signals. The results show that the proposed method can effectively identify the bearing state.

The contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent (LRD) process described by two independent parameters: Hurst parameter H and fractal dimension D. Compared with the fractional Brownian motion (fBm) with a linear relationship between H and D, the GC process can more flexibly describe various LRD processes. Before building the forecasting model, this article demonstrates the GC process using H and D to describe the LRD and fractal properties of stochastic sequences, respectively. The GC process is taken as the diffusion term to establish a differential iterative forecasting model, where the incremental distribution of the GC process is obtained by statistics. The parameters of the forecasting model are estimated by the box dimension, the rescaled range, and the maximum likelihood methods. Finally, a real wind speed data set is used to verify the performance of the GC difference iterative forecasting model.

In this study, we examined a tank container for foodstuff that is generally used for the transport of foodstuffs. With the aid of the “ANSYS R17.0” program code, a numerical model of the tank container for foodstuffs was realized. Further, to validate the considered model, the tank container considered was submitted to the most important ISO tests concerning both its support frame and the tank. The results obtained from the FEM analysis, in terms of displacement for each test, were compared with those provided by the manufacturer and related to the tank container considered, evaluating the difference between the numerical results with the experimental ones. This allowed us to validate the model examined. Furthermore, the results obtained from each test, in terms of stress, have made it possible to locate the areas with the highest equivalent stress and quantify the maximum value, comparing it with the allowable stress. In this way, a better understanding of the structure was achieved, and it was detected that the most stressed area is that of the connections between the container and the frame. Furthermore, modal analysis was carried out, in which the natural frequencies relating to the most dangerous modes of vibrations were found, that is, with the lowest frequency values. Finally, changes for the considered tank container were examined, and it was found that, by changing parameters, such as the thickness of the plate and skirt, and subsequently acting on the arrangement of the corner supports, the highest value of the stresses generated by the loads related to the ISO tests, it is significantly lowered, resulting in a better distributed stiffening of the structure and a reduction, although minimal, of weight. It is evident that this modeling and validation method, suitably integrated by further calculation modules, can be used in an iterative optimization process.

Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver’s status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.

This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results.

In recent years, driving simulators have been widely used by automotive manufacturers and researchers in human-in-the-loop experiments, because they can reduce time and prototyping costs, and provide unlimited parametrization, more safety, and higher repeatability. Simulators play an important role in studies about driver behavior in operating conditions or with unstable vehicles. The aim of the research is to study the effects that the force feedback (f.f.b.), provided to steering wheel by a lane-keeping-assist (LKA) system, has on a driver’s response in simulators. The steering’s force feedback system is tested by reproducing the conditions of criticality of the LKA system in order to minimize the distance required to recover the driving stability as a function of set f.f.b. intensity and speed. The results, obtained in three specific criticality conditions, show that the behaviour of the LKA system, reproduced in the simulator, is not immediately understood by the driver and, sometimes, it is in opposition with the interventions performed by the driver to ensure driving safety. The results also compare the performance of the subjects, either overall and classified into subgroups, with reference to the perception of the LKA system, evaluated by means of a questionnaire. The proposed experimental methodology is to be regarded as a contribution for the integration of acceptance tests in the evaluation of automation systems.

In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the longitudinal directions are ignored for the magnetic induction strength and temperature, and only the axial calculation is performed. Therefore, we propose the electromagnetic-thermal coupled three-dimensional model of eddy current welding. The induced eddy current heat is obtained by adding the z-axis spatial angle to the two-dimensional electromagnetic-thermal, thus obtaining some new results by coupling the numerical simulation and computations of the electric field and magnetic induction intensity of the three-dimensional model. Moreover, we have considered the objective function into a weak formulation. The three-dimensional model is then meshed by the finite element method. The electromagnetic-thermal coupling has been numerically computed, and the parametric dependence to the eddy current heating process has been fully studied. Through the numerical simulation with different current densities, frequencies, and distances, the most suitable heat treatment process of U75V rail is obtained.

In this study, fluid−structure interaction (FSI) modeling was applied for predicting the fluid flow in a specific peristaltic pump, composed of one metallic roller and a hyperelastic tube pumping a viscous Newtonian fluid. Hyperelastic material dynamics and turbulence flow dynamics were coupled in order to describe all the physics of the pump. The commercial finite element software ABAQUS 6.14 was used to investigate the performance of the pump with a 3D transient model. By using this model, it was possible to predict the von Mises stresses in the tube and flow fluctuations. The peristaltic pump generated high pressure and flow pulses due to the interaction between the roller and the tube. The squeezing and relaxing of the tube during the operative phase allowed the liquid to have a pulsatile behavior. Numerical simulation data results were compared with one cycle pressure measurement obtained from pump test loop data, and the maximum difference between real and simulated data was less than 5%. The applicability of FSI modeling for geometric optimization of pump housing was also discussed in order to prevent roller and hose parts pressure peaks. The model allowed to investigate the effect of pump design variations such as tube occlusion, tube diameter, and roller speed on the flow rate, flow fluctuations, and stress state in the tube.

The purpose of this research was to determine the optimal geometry of a variable pitch conical helicoid to be used in a pressing machine for grape pomace, also known as grape marc. This study attempted to understand if the optimized geometry of the considered helicoid after every pitch resulted in volume decrease DVc, equal to that obtained during the pressing phase of grape pomace DVp, using an optimized membrane press. The conical helicoid with variable pitch was replaced in a machine that offered continuous pressing of grape pomace using a cylindrical helicoid with constant pitch (constant pressure distribution, not optimized, along the cochlea axis). As this was a machine already available in the market, the overall dimensions were already established—5.95 m in length and 1.5 m in width. The pressure distribution p1 and volume change DVp, obtained during the grape pomace pressing phase in the optimized membrane press (producing high-quality wine) was taken into consideration in this research. Furthermore, the optimized pressure distribution p1 was applied in seven phases during the pressing process, and a consequent volume change value DVp was obtained for each phase. Therefore, this study determined the geometry of the variable pitch conical helicoid, which, after every pitch, resulted in volume changing DVc that was similar to the volume changing DVp obtained by the optimized membrane press. For this scope, calculations were realized using the Mathematica 10 program code, which, on being assigned the overall dimensions, slope angle of the helicoid, and volume for the first pitch value, determined the radius and pitch values of the helicoid, total volume, and volume change DVc. It was also noted that by appropriately varying the geometric parameters (taper and pitch of the helicoid), different options of pressure distribution on grape pomace can be obtained, thus enabling improvement and optimization of product quality.

Mechanical parameters of the olive wood plate have been computed by data inversion of vibrational experimental tests. A numerical-experimental method has allowed the evaluation of the two transverse shear moduli and the four in-plane moduli of a thick orthotropic olive tree plate. Therefore, the natural flexural vibration frequencies of olive trees plates have been evaluated by the impulse technique. For our purposes, we define the objective function as the difference between the numerical computation data and the experimental ones. The Levenberg−Marquardt algorithm was chosen as optimization strategy in order to minimize the matching error: the evaluation of the objective function has required a complete finite element simulation by using the ANSYS code. As input, we have used the uniaxial test data results obtained from the olive plates. The converged elastic moduli with n = 10 natural modes were E1 = 14.8 GPa, E2 = 1.04 GPa, G12 = 4.45 GPa, G23 = 4.02 GPa, G13 = 4.75 GPa, ν12 = 0.42, and ν13 = 0.42. The relative root mean square (RMS) errors between the experimental frequencies and the computed one is 9.40%. Then, it has been possible to obtain a good agreement between the measured and calculated frequencies. Therefore, it has been found that for plates of moderate thickness the reliability of the estimated values of the transverse shear moduli is good.

After briefly recalling the main problems that arise in the study of globe valves for alternative pumps, a methodology has been set up in order to refine the design. The obtained method has the advantages of simplicity and independence from empirical diagrams. In summary, from the obtained equation, the suitable values of the parameters can be deduced, based on the assigned data (capacity Q0 and number of rounds n) of all the dimensions of the valve or of the valves. Depending on the parameter values, it is possible to identify the most suitable kind of valve: a single dish-shaped valve, a ring valve, a valve with several rings or a group of valves.

In this article, the information value is used in numeric analysis as both a method for data approximation and a measure of data equality among a set of values. To this end, a surface segmentation, based on a study for constructing a hierarchy for vectors clustering using certain similarity criteria, is presented. The technique is based on the analysis of vectors representing regions associated with given types of critical points. An approach based on the Max Entropy in Metric Space (MEMS) is introduced in the paper, in order to extract a cluster of local features and to obtain an analysis of mechanical systems in the 2D and/or 3D spaces. The approach proposed in the paper can be effectively used in virtual prototyping and optimal designing of mechanical systems.

This research aimed to determine the effects of cryo-maceration at different temperatures on polyphenol content during the winemaking process of Chardonnay wine. Samples of Chardonnay grapes were subjected to rapid cooling processes by direct injection of liquid CO2 to obtain final temperatures of 10.0, 8.0, 6.0 and 4.0 °C and yield different batches of grape mash. Subsequently, each batch underwent the winemaking process to produce four different wines. The wines obtained were characterized by chemical analyses. We observed higher extraction of polyphenolic compounds with low-temperature cold maceration, particularly when the temperature was reduced from 10.0 to 6.0 °C. Conversely, when the temperature was reduced below 6.0 °C, the increase in polyphenol content in wine was negligible, whereas CO2 consumption increased. Furthermore, a numerical simulation was performed to determine the pipe length, L0, after which the temperature was constant. This condition is very important because it guarantees that after the length L0, the thermodynamic exchange between liquid CO2 and is complete, eliminating the possibility of liquid CO2 pockets in the cyclone.

In this paper, the problem of the evaluation of the uncertainties that originate in the complex design process of a new system is analyzed, paying particular attention to multibody mechanical systems. To this end, the Wiener-Shannon’s axioms are extended to non-probabilistic events and a theory of information for non-repetitive events is used as a measure of the reliability of data. The selection of the solutions consistent with the values of the design constraints is performed by analyzing the complexity of the relation matrix and using the idea of information in the metric space. Comparing the alternatives in terms of the amount of entropy resulting from the various distribution, this method is capable of finding the optimal solution that can be obtained with the available resources. In the paper, the algorithmic steps of the proposed method are discussed and an illustrative numerical example is provided.

In this paper, the use of the MaxInf Principle in real optimization problems is investigated for engineering applications, where the current design solution is actually an engineering approximation. In industrial manufacturing, multibody system simulations can be used to develop new machines and mechanisms by using virtual prototyping, where an axiomatic design can be employed to analyze the independence of elements and the complexity of connections forming a general mechanical system. In the classic theories of Fisher and Wiener-Shannon, the idea of information is a measure of only probabilistic and repetitive events. However, this idea is broader than the probability alone field. Thus, the Wiener-Shannon’s axioms can be extended to non-probabilistic events and it is possible to introduce a theory of information for non-repetitive events as a measure of the reliability of data for complex mechanical systems. To this end, one can devise engineering solutions consistent with the values of the design constraints analyzing the complexity of the relation matrix and using the idea of information in the metric space. The final solution gives the entropic measure of epistemic uncertainties which can be used in multibody system models, analyzed with an axiomatic design.

Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.