Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine)

When a system’s performance is inadequate, the concept of availability importance can be used to improve it. The availability of an item depends on the combined aspects of its reliability and maintainability. In a system consisting of many subsystems, the availability of some subsystems is more important to system performance than others. The availability measure determines the priority of availability across subsystems. Most researchers only consider operation time and ignore the influence of the operating environment; therefore, their estimations are not accurate enough. In contrast to previous research, we focus on the influence of the operating environment on the system/subsystem’s characteristics with a view to prioritizing them based on the importance of availability. The paper considers part of the mining fleet system of Sungun copper mine, including the wagon drill, loader, bulldozer, and dump truck subsystems. We identify an ordered list of possibilities for availability improvement and suggest changes or remedial actions for each item to either reduce its failure rate or reduce the time required to repair it.     

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

A method to generate a suite of artificial near‐fault ground motion time histories for specified earthquakes is presented. A wavelet‐based nonstationary (WB‐NS) model has been employed to effectively capture the time‐varying frequency content of a particular acceleration record and continuous wavelet transform has been used to simulate the largest velocity pulse. Furthermore, an iterative procedure using discrete wavelet transform is utilized to modify an earthquake ground motion and generate energy‐compatible ground motion. Eventually, the artificial near‐fault accelerogram is achieved via the superposition of a coherent extracted velocity pulse with a random acceleration record corresponding to a WB‐NS model and multiplied by a time‐modulating envelope function. The effectiveness of the method is demonstrated by comparing the spectral response and Arias intensity curves of the simulated accelerograms with those of the real records.     

Effectiveness of modified pushover analysis procedure for the estimation of seismic demands of buildings subjected to near‐fault earthquakes having forward directivity

Near‐fault ground motions with long‐period pulses have been identified as being critical in the design of structures. These motions, which have caused severe damage in recent disastrous earthquakes, are characterized by a short‐duration impulsive motion that transmits large amounts of energy into the structures at the beginning of the earthquake. In nearly all of the past near‐fault earthquakes, significant higher mode contributions have been evident, resulting in the migration of dynamic demands (i.e., drifts) from the lower to the upper stories. Due to this, the static nonlinear pushover analysis (PA) (which utilizes a load pattern proportional to the shape of the fundamental mode of vibration) may not produce accurate results when used in the analysis of structures subjected to near‐fault ground motions. The objective of this paper was to improve the accuracy of the pushover method in these situations by introducing a new load pattern into the common pushover procedure. Several PAs are performed for six existing reinforced concrete buildings that possess a variety of natural periods. Then, a comparison is made between the PA results (with four new load patterns) and those of FEMA‐356 with reference to nonlinear dynamic time‐history analyses. The comparison shows that, generally, the proposed pushover method yields better results than all FEMA‐356 PA procedures for all investigated response quantities, and is a closer match to the nonlinear time‐history responses. In general, the method is able to reproduce the essential response features providing a reasonable measure of the likely contribution of higher modes in all phases of the response. Copyright © 2009 John Wiley & Sons, Ltd.     

Context-driven mean residual life estimation of mining machinery

Maintenance is crucial to ensure production/output and customer satisfaction in the mining sector. The cost of maintenance of mechanised and automated mining systems is very high, necessitating efforts to enhance the effectiveness of maintenance systems and organisation. For effective maintenance planning, it is important to have a good understanding of the reliability and availability characteristics of the systems. Determining the Mean Residual Life (MRL) of systems allows organisations to more effectively plan maintenance tasks. In this paper, we use a statistical approach to estimate MRL and consider a Weibull proportional hazard model (PHM) with time-independent covariates to model the hazard function so that the operating environment could be integrated into the reliability analysis. The paper explains our methods for calculating the conditional reliability function and computing the MRL as a function of the current conditions. The model is verified and validated using data from the hydraulic system of LHD equipment in a Swedish mine. The results are useful to estimate the remaining useful life of such systems; the method can be used for maintenance planning, helping to control unplanned stoppages of highly mechanised and automated systems.     

State Estimation For An Agonistic‐Antagonistic Muscle System

Research on assistive technology, rehabilitation, and prosthetics requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic‐antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the convergence of the three observers. Numerical simulations reveal that the three observers are comparable and provide reliable estimates.     

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informativeness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because (i) it re-uses the same features extracted for detection, (ii) it aggregates features using integral images, and (iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that DeepProposals outperform most of the previous object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.     

Al2O3–Water nanofluid heat transfer and entropy generation in a ribbed channel with wavy wall in the presence of magnetic field

In this article, a parametric study is conducted to evaluate heat transfer enhancement in a ribbed channel containing Al₂O₃–Water nanofluid with wavy wall. The physical domain is under the influence of the magnetic field that creates a negative force against the working fluid to move. Nanofluid with higher temperature enters the cool ribbed duct and heat is exchanged along the walls of channel. The effects of the dominant parameters including number of the blocks, solid volume fractions of nanofluid, Hartmann number, Reynolds number, and different states of amplitude sine waves are numerically tested on the local and average Nusselt number, skin friction, and total entropy generation. Excellent agreement between present study and previous literature is observed. It is found that, an augmentation in magnetic field will result in higher values of both local and average Nusselt number accompanying with bigger values of skin friction and entropy generation. Computations illustrate that, increasing the solid volume fraction of the Al₂O₃ nanoparticles will raise the Nusselt number and total entropy generation rate but its effect on the skin friction is negligible. Also, numerical results imply that increasing amplitude sine waves of the geometry has incremental effect on the Nusselt number and skin friction but its effect on the total entropy generation rate is not so clear. Moreover, by adding number of the used blocks in the presence of magnetic field, the local Nusselt number experiences more jumps but it does not increase the average Nusselt number, necessarily. In addition, using more blocks increases skin friction but it has a reverse effect on the total entropy generation rate.