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This research presents the theory and implementation detail for the extraction of angular speed variations. In particular, band‐pass filtering, frequency shifting and analytic representation are addressed with simple mathematical equations. Finally, some case studies on a 3kW induction motor are investigated for healthy and faulty conditions. Some vital key features and characteristics can be extracted from the angular speed variations. Pole pass speed side bands around the rotor speed can be used as a feature for broken rotor bar faults. The number of poles multiplied by the synchronous running speed cycles and the rise of 100 Hz components are key characteristics for voltage imbalance faults.

Granular material exhibits rich dynamical behaviors under impacting, and its impacting dynamical process is seriously influenced by many factors. The purpose of this paper is to explore the dynamical response of granular bed obliquely impacted by a rotational projectile, and the effect of density ratio and diameter ratio on its penetration depth is mainly considered.

In most experiments, as the angular velocity and the impact velocity always produce a coupling effect on the whole impact process, then it is quite difficult to separately distinguish the influence of angular velocity. Therefore, the discrete element method is used here to achieve this purpose. The authors vary one parameter and keep other parameter unchanged, and then discuss the effect of these parameters on penetration depth statistically.

The numerical model in this paper can effectively predict the dynamical process of granular medium under impacting. The projectile’s penetration depth exhibits a similar scaling with its angular velocity under different density ratios and diameter ratios, and the angular velocity exhibits an obvious criticality.

A DEM code and corresponding statistical approach are used to explore the complex dynamical process of a granular material obliquely impacted by a rotation projectile.

This paper investigates the load carrying capacity of the journal bearings with steel shafts with varying surface texture in varying revolutions using experimental and neural network (NN) approach.

In this study, we used a shaft with smooth surface with the same material properties compare their load carrying capacities of the shafts with three different pitches and two different profiles. The experimental data, such as pressure and oil temperature, are employed as training and testing data for NN. Quick Prop algorithm is used to update the weight of the network during the training.

The designated NN has superior performance for modelling of the system. Therefore, the proposed neural predictor would be used as a predictor for possible experimental applications on modelling bearing system.

Mobil 0W‐40 lubricant was used and kept at temperature of 18°C. The surface of the shafts has been in two types: smooth, that is without and with profiles, that is trapezoidal and saw.

Owing to the parallel structure and fast learning of NN, this kind of algorithm will be utilized to model other types of bearing systems.

Instead of traditional methods, NN has fast learning and parallel processing structure. Moreover, NN can be used to process multiple‐input/multiple‐output data unlike other empirical modelling tools which can map one dependent variable at a time. Therefore, this method is able to predict the load carrying capacity with steel shafts with varying surface texture in varying revolutions satisfactorily where common techniques have failed.

The purpose of this paper is to establish the dynamics model of a Z-folded PhoneSat considering hinge friction and to investigate the influence of disturbances, such as friction, stiffness asymmetry, deployment asynchronicity and initial disturbance angular velocity, on the attitude of PhoneSat during and after deployment.

For the Z-folded PhoneSat, the dynamics model considering hinge friction is established and the dynamics simulation is carried out. The effects of friction, stiffness asymmetry, deployment asynchronicity and initial disturbance angular velocity on the attitude motion of the PhoneSat are studied and the attitude motion regularities of the PhoneSat considering the disturbance factors mentioned above are discussed.

Friction has a main contribution to reducing the oscillation of attitude motion and damping out the residual oscillation, ultimately decreasing the deployment time. An increasing length of deployment time is required with the increasing stiffness asymmetry and time difference of asynchronous deployment, which also have slight disturbances on the attitude angle and angular velocity of PhoneSat after the deployment. The initial disturbance angular velocity in the direction of deployment would be proportionally weakened after the deployment, whereas initial disturbance angular velocity in other direction induces angular velocities of other axes, which dramatically enhances the complexity of attitude control.

The paper is a useful reference for engineering design of small satellites attitude control system.

This paper aims to present combination of poly-jet technology and ink-jet technology in a multidisciplinary way in order to exploit advantages of these rapid prototyping techniques in manufacturing a demonstrator device – a variable interdigital capacitor.

The platform of 3D complex geometry, with optimized design and cavity under the capacitor's fingers (plates), was fabricated using Alaris 3D printer, whereas silver conductive segments were fabricated using Dimatix ink-jet printer and thanks to the mechanical flexibility the platform has been covered using these segments.

When one side of the capacitor's structure changes angular position (in the range from 0 to 90°) with reference to the fixed part, the variation in total capacitance is obtained. The total capacitance decreases (in the range from 20.2 to 1.5 pF) with decrease in effective overlapping area for the variation of angular position from 0 to 90° The maximum measured tuning ratio for the proposed design of the variable capacitor was 13.5:1.

Presented variable capacitor can be used for detection angular position in the range from 0 to 90°.

The new horizon has been opened combining the rapid prototyping equipment in electronics and mechanical engineering in an interdisciplinary way to manufacture, for the first time, variable capacitor using poly-jet and ink-jet technologies. These techniques do not require higher mask counts which makes the fabrication fast and cost-effective.

This work, for the first time, demonstrates the combination of ALARIS 30 3D printer and Dimatix DMP-3000 materials deposition printer in order to fabricate the interdigital capacitor with complex 3D geometry. ALARIS 3D printer has been used for manufacturing plastic platform (with the possibility to precisely adjust angular position of one comb related to another) and Dimatix printer has been used to print silver conductive inks on flexible substrates (Kapton film), and this mechanically flexible structure was used to cover capacitor's fingers on the platform (assembly).

This paper investigates the pressure variations on the steel shafts on the journal bearing system with low temperature and variable speed. This paper mainly consist of two parts, experimental and simulation. In the experimental work, journal bearing system is tested with different shafts speed and temperature conditions. The temperature of the system's working conditions was under minus. The collected experimental data such as pressure variations are employed as training and testing data for an artificial neural network. The neural network is a feed forward three layered network. Quick propagation algorithm is used to update the weight of the network during the training. Finally, neural network predictor has superior performance for modelling journal bearing systems with load disturbances.

The influence of the cam angular speed on the pressure, film thickness and temperature profiles at some selected angular positions together with the oil characteristics are investigated.

A high-order polynomial cam is used, and thermal elastohydrodynamic lubrication (EHL) calculations are carried out by the multi-grid method and line-line scanning technique.

It is found that the film thickness decreases with a decrease in angular speed. The depth of the dimple that occurred in the reverse motion is also reduced because of the recession in the “temperature–viscosity wedge” effect.

It is revealed that the reduction in the cam angular speed makes the classical big surface dimple evolve into a small centralized dimple during the opposite sliding motion.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0327

The Remote Center Compliance (RCC) device is a mechanical device which acts as a multi‐axis float to achieve lateral and angular alignment between components which are being mated during assembly operations. The device has been evaluated for assembly applications and its operational characteristics and limitations in achieving automatic mechnical alignment are described.

The purpose of this paper is to investigate pressure distribution of the journal bearings with aluminium shafts with varying surface porosity in varying revolutions using experimental and neural network approach.

The collected experimental data such as pressure variations is employed as training and testing data for an artificial neural network (ANN). Back propagation algorithm is used to update the weight of the network during the training.

Neural network predictor has superior performance for modelling journal bearing systems with shafts of different surface porosities.

Back propagation algorithm is used training algorithm for proposed neural networks. Various training algorithms can be used to train proposed network. The spectrum of the journal surface porosity can be enlarged.

From the experimental and simulation results, neural network exactly follows the experimental results. Because of that, this kind of neural network predictors can be applied on journal bearing systems in practice applications.

This paper discusses a new modelling scheme known as ANNs. A neural network predictor has been employed to analyze of the effects of shaft surface porosity in hydrodynamic lubrication of journal bearing.

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.