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The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

The purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).

A new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.

By examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.

The proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.

It is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

Cemented conglomerate accumulation is a weak and heterogeneous medium that occurs in western China. It consists mainly of argillaceous cement that loses strength rapidly upon contact with water, leading to collapse instability failure. Its deformation failure mechanism is complex and poorly understood. In this paper, the erosion failure mechanism of cemented conglomerate accumulation is investigated.

The collapse failure process after erosion of the slope foot for typical cemented conglomerate accumulation is studied based on field investigation using the particle discrete element method. And how the medium composition, slope angle and cementation degree influence the failure mode and process of the cemented conglomerate accumulation is examined.

The foot erosion of slope induces a tensile failure that typically manifests as “erosion at the foot of slope – tensile cracking at the back edge of slope top – integral collapse.” The collapse failure is more likely to occur when the cemented conglomerate accumulation has a higher rock content, a steeper slope angle or a weaker cementation degree.

A model based on rigid blocks and disk particles to simulate the cemented conglomerate accumulation is developed. It shows that the hydraulic erosion at the foot of the slope resulted in a different failure mechanism than that of general slopes. The results can inform the stability management, disaster prevention and mitigation of similar slopes.

This study aims to model and investigate low-loss wave-propagation modes across random media. The objective is to achieve better channel properties for applying radio links through random vegetation (e.g. forest) using a beamforming approach. Thus, obtaining the link between the statistical parameters of the media and the channel properties.

A beamforming approach is used to obtain low-loss propagation across random media constructed of long cylinders, i.e. a simplified two dimensional (2D) model of agroforests. The statistical properties of the eigenmode radio wave propagation are studied following a Monte Carlo method. An error quantity is defined to represent the robustness of an eigenmode, and it is shown that it follows a known Lognormal statistical distribution, thereby providing a base for further statistical investigations.

In this study, it is shown that radio wave propagation eigenmodes exist based on a mathematical model. The algorithm presented can find such modes of propagation that are less affected by the statistical variation of the media than the regular beams used in radio wave communication techniques. It is illustrated that a sufficiently chosen eigenmode waveform is not significantly perturbed by the natural variation of the tree trunk diameters.

As a new approach to obtain low-loss propagation in random media at microwave frequencies, the presented mathematical model can calculate scattering-free wave-propagation eigenmodes. A robustness quantity is defined for a specific eigenmode, considering a 2D simplified statistical forest example. This new robustness quantity is useful for performing computationally low-cost optimization problems to find eigenmodes for more complex vegetation models.

This research paper aims to investigate the change detection filter technique with a decision tree-based event (fault type) classifier for recognizing and categorizing power system disturbances on the high-voltage DC (HVDC) transmission link.

A change detection filter is used to the average and differential current components, which detects the point of fault initiation and records a change detection point (CDP). The half-cycle differential and average currents on both sides of the CDP are sent through the signal processing unit, which produces the respective target. The extracted target indices are sent through a decision tree-based fault classifier mechanism for fault classification.

In comparison with conventional differential current protection systems, the developed framework is faster in fault detection and classification and provides great accuracy. The new technology allows for prompt identification of the fault category, allowing electrical grids to be restored as quickly as possible to minimize economic losses. This novel technology enhances efficiency in terms of reducing computing complexity.

Setting a threshold value for identification is one of the limitations. To bring the designed system into stability condition before creating faults on it is another limitation. Reducing the computational burden is one of the limitations.

Creating a practical system in laboratory is difficult as it is a HVDC transmission line. Apart from that, installing rectifier and converter section for HVDC transmission line is difficult in a laboratory setting.

The suggested scheme’s importance and accuracy have been rigorously validated for the standard HVDC transmission system, subjected to various types of DC fault, and the results show the proposed algorithm would be a feasible alternative to real-time applications.

This paper aims to develop a model that reflects the current transformer (CT) core materials nonlinearity. The model enables simulation and analysis of the CT excitation current that includes the inductive magnetizing current and the resistive excitation current.

A nonlinear CT model is established with the magnetizing current as the solution variable. This model presents the form of a nonlinear differential equation and can be solved discretely using the Runge–Kutta method.

By simulating variations in the excitation current for different primary currents, loads and core materials, the results demonstrate that enhancing the permeability of the B–H curve leads to a more significant improvement in the CT ratio error at low primary currents.

The proposed model has three obvious advantages over the previous models with the secondary current as the solution variable: (1) The differential equation is simpler and easier to solve. Previous models contain the time differential terms of the secondary current and excitation flux or the integral term of the flux, making the iterative solution complicated. The proposed model only contains the time differential of the magnetizing current. (2) The accuracy of the excitation current obtained by the proposed model is higher. Previous models calculate the excitation current by subtracting the secondary current from the converted primary current. Because these two currents are much greater than the excitation current, the error of calculating the small excitation current by subtracting two large numbers is greatly enlarged. (3) The proposed model can calculate the distorted waveform of the excitation current and error for any form of time-domain primary current, while previous models can only obtain the effective value.

Fix-position preloading, centrifugal force and higher temperatures cause the bearing units in angular contact ball bearings to expand, changing the contact load and affecting bearing life. This study aims to examine the effect of thermal and centrifugal expansion on the fatigue life of fix-position preloaded angular contact ball bearings in high-speed operating conditions.

The contact loads on the inner and outer bearing rings were resolved according to the thermal and centrifugal expansion factors in the quasi-static position preloading model. The pressure and frictional stress distribution were used to calculate the subsurface stress in the contact area, while the Zaretsky model was used to determine the relative fatigue life of the inner and outer bearing rings.

Under fix-position bearing preloading, thermal and centrifugal expansion significantly affected the contact load and relative fatigue life. At the same axial preload, the inner ring contact load was higher than the outer ring contact load, with a maximum difference of 132.3%. The decrease in the inner ring relative life exceeded the outer ring contact load, with a maximum difference of 7.5%, compared to the absence of thermal and centrifugal expansion.

This study revealed the influence of thermal and centrifugal expansion on the fatigue life of angular contact ball bearings in high-speed service conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0065/

This paper aims to discuss the inverse problems that arise in various practical heat transfer processes. The purpose of this paper is to provide an identification method for predicting the internal boundary conditions for thermal analysis of mechanical structure. A few examples of heat transfer systems are given to illustrate the applicability of the method and the challenges that must be addressed in solving the inverse problem.

In this paper, the thermal network method and the finite difference method are used to model the two-dimensional heat conduction inverse problem of the tube structure, and the heat balance equation is arranged into an explicit form for heat load prediction. To solve the matrix ill-conditioned problem in the process of solving the inverse problem, a Tikhonov regularization parameter selection method based on the inverse computation-contrast-adjustment-approach was proposed.

The applicability of the proposed method is illustrated by numerical examples for different dynamically varying heat source functions. It is proved that the method can predict dynamic heat source with different complexity.

The modeling calculation method described in this paper can be used to predict the boundary conditions for the inner wall of the heat transfer tube, where the temperature sensor cannot be placed.

This paper presents a general method for the direct prediction of heat sources or boundary conditions in mechanical structure. It can directly obtain the time-varying heat flux load and thtemperature field of the machine structure.

This paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.

Based on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.

By building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.

The idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.