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Machines, Volume 6, Issue 4 (December 2018)

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Open AccessArticle Full-Scale Wind Turbine Vibration Signature Analysis
Received: 7 November 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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Abstract
A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was
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A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox. Full article
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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Open AccessArticle Smart Hybrid Manufacturing Control Using Cloud Computing and the Internet-of-Things
Received: 31 October 2018 / Revised: 27 November 2018 / Accepted: 28 November 2018 / Published: 3 December 2018
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Abstract
Industry 4.0 is expected to deliver significant gains in productivity by assimilating several technological advancements including cloud computing, the Internet-of-Things, and smart devices. However, it is unclear how these technologies should be leveraged together to deliver the promised benefits. We present the architecture
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Industry 4.0 is expected to deliver significant gains in productivity by assimilating several technological advancements including cloud computing, the Internet-of-Things, and smart devices. However, it is unclear how these technologies should be leveraged together to deliver the promised benefits. We present the architecture design of an information system that integrates these technologies to support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. We show how well-structured architecture design is the basis for a modular, complex cyber-physical system that provides horizontal, cross-functional manufacturing process management and vertical control of heterogenous work cells. The modular nature allows the extensible cloud support enhancing its accessibility to small and medium enterprises. The information system is designed as part of the HORSE Project: a five-year research and innovation project aimed at making recent technological advancements more accessible to small and medium manufacturing enterprises. The project consortium includes 10 factories to represent the typical problems encountered on the factory floor and provide real-world environments to test and evaluate the developed information system. The resulting information system architecture model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As a reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template to develop commercial-grade manufacturing applications for Industry 4.0. Full article
(This article belongs to the Special Issue Smart Manufacturing)
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Open AccessArticle Tether Space Mobility Device Attitude Control during Tether Extension and Winding
Received: 30 September 2018 / Revised: 17 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
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Abstract
Recently, advancements in space technology have opened up more opportunities for human beings to work in outer space. It is expected that upsizing of manned space facilities, such as the International Space Station, will further this trend. Therefore, a unique means of transportation
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Recently, advancements in space technology have opened up more opportunities for human beings to work in outer space. It is expected that upsizing of manned space facilities, such as the International Space Station, will further this trend. Therefore, a unique means of transportation is necessary to ensure that human beings can move about effectively in microgravity environments. In the present study, we propose a tether-based mobility system, which moves the user by winding a tether attached to a structure at the destination. However, there is a problem in that the attitude of the user becomes unstable during winding of the tether. Therefore, a Tether Space Mobility Device (TSMD) attitude control method for winding a tether is examined through numerical analysis. The proposed analytical model consists of one flexible body and three rigid bodies. The contact force between the tether and the inlet is considered. We verified the validity of the proposed model through experiments. Furthermore, we proposed a TSMD attitude control method during tether winding while focusing on changes in the system’s rotational kinetic energy. Using the proposed analytical model, the angular velocity of a rigid body system is confirmed to converge to 0 deg/s when control is applied. Full article
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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Open AccessArticle Innovative Urban Transportation Means Developed by Integrating Design Methods
Received: 2 October 2018 / Revised: 9 November 2018 / Accepted: 13 November 2018 / Published: 21 November 2018
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Abstract
The aim of this article is to apply some design methodologies to define, as a first objective, an optimized technical specification and then, as a second objective, to manage the transition from conceptual design to construction project of an innovative means of urban
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The aim of this article is to apply some design methodologies to define, as a first objective, an optimized technical specification and then, as a second objective, to manage the transition from conceptual design to construction project of an innovative means of urban transport, meeting the needs of ‘renewable energy’ requirements, which then decline into this new urban vehicle formed by a hoverboard and an electric scooter. The first part of the article is focused on the conceptual design of the means by using methodologies such as the Quality Function Deployment (QFD), applied in the first phase of the work to compare some of the most popular electric scooters on the market; we then used a typical method for product marketing, i.e., the decision-making process driven by the analysis of benchmarking, suitable for quantitatively organize competitive analysis and choosing innovation targets; finally, we implemented the top-flop analysis in order to better improve the benchmarking implementation, identifying the best product on the market, basing on the highest number of innovative requirements owned by it, as shown by Frizziero in 2018 and Meuli et al. in 1997. The second part of the article focuses on the project of the kick scooter through the use of a software for the FEA simulation and on the possible realization of the prototype through a suitable connecting component. Full article
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Open AccessFeature PaperArticle Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks
Received: 23 October 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
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Abstract
The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount
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The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines. Full article
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Open AccessArticle Equipment and Technology for Combined Ion–Plasma Strengthening of Cutting Tools
Received: 17 October 2018 / Revised: 4 November 2018 / Accepted: 6 November 2018 / Published: 9 November 2018
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Abstract
A combined strengthening of cutting tools for finishing has been carried out in glow discharge plasma filling a process vacuum chamber. At the first stage, reamers rotating around the axis distanced from the magnetron targets at 8 cm were bombarded by fast argon
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A combined strengthening of cutting tools for finishing has been carried out in glow discharge plasma filling a process vacuum chamber. At the first stage, reamers rotating around the axis distanced from the magnetron targets at 8 cm were bombarded by fast argon atoms produced due to charge exchange collisions of ions accelerated in space charge sheathes between the plasma and a negatively biased to 3 kV grid with a 25 cm radius of its concave surface curvature. The reamer bombardment by fast neutral atoms led to a reduction of its cutting-edge radius from ~7 μm to ~2 μm. At the second stage, the reamer surface was nitrided within 1 h at a temperature of 500 °C stabilized by regulation of the negative bias voltage accelerating the nitrogen ions. At the third stage, a 3 μm thick TiN coating has been synthesized on the reamer bombarded by pulsed beams of 3 keV neutral atoms at a 50 Hz repetition rate of 50 μs wide pulses. After the combined strengthening, the cutting edge radius of the coated reamer amounted to ~5 μm and the roughness of the area machined by the reamer holes in blanks made of structural steel reduced by about 1.5 times. Full article
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Open AccessArticle Topology Choice and Optimization of a Bearingless Flux-Switching Motor with a Combined Winding Set
Received: 1 September 2018 / Revised: 18 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
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Abstract
The purpose of this paper is to choose a new topology for bearingless flux-switching slice motors, regarding the number of stator and rotor poles, with a combined winding set. Additionally, the selected motor topology is optimized with finite element method (FEM) simulations to
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The purpose of this paper is to choose a new topology for bearingless flux-switching slice motors, regarding the number of stator and rotor poles, with a combined winding set. Additionally, the selected motor topology is optimized with finite element method (FEM) simulations to improve the performance. Bearingless slice drives feature a magnetically-suspended rotor disk passively stabilized by reluctance forces due to a permanent magnet (PM) bias flux in the air gap and actively controlled by the generation of radial bearing forces and motor torque. Usage of the combined winding set, where each phase generates both motor torque and suspension forces, opens the opportunity for a new topology. The topology choice and optimization are based on FEM simulations of several motor optimization criteria, as the passive axial, tilting and radial stiffness values and the active torque and bearing forces, which are simulated regarding the motor height and specific stator and rotor parameters. Saturation, cogging torque and cogging forces are also analyzed. The 3D FEM program ANSYS Maxwell 2015 was used. The results led to an optimized bearingless flux-switching motor topology with six new stator segments and seven rotor poles. By optimizing the geometry, a considerable improvement of performance was reached. This geometry optimization is a base for a future prototype model. Full article
(This article belongs to the Special Issue High Speed Motors and Drives: Design, Challenges and Applications)
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Open AccessArticle The Modelling, Simulation and FPGA-Based Implementation for Stepper Motor Wide Range Speed Closed-Loop Drive System Design
Received: 27 August 2018 / Revised: 12 October 2018 / Accepted: 22 October 2018 / Published: 1 November 2018
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Abstract
Owing to the benefits of programmable and parallel processing of field programmable gate arrays (FPGAs), they have been widely used for the realization of digital controllers and motor drive systems. Furthermore, they can be used to integrate several functions as an embedded system.
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Owing to the benefits of programmable and parallel processing of field programmable gate arrays (FPGAs), they have been widely used for the realization of digital controllers and motor drive systems. Furthermore, they can be used to integrate several functions as an embedded system. In this paper, based on Matrix Laboratory (Matlab)/Simulink and the FPGA chip, we design and implement a stepper motor drive. Generally, motion control systems driven by a stepper motor can be in open-loop or closed-loop form, and pulse generators are used to generate a series of pulse commands, according to the desired acceleration/run/deceleration, in order to the drive system to rotate the motor. In this paper, the speed and position are designed in closed-loop control, and a vector control strategy is applied to the obtained rotor angle to regulate the phase current of the stepper motor to achieve the performance of operating it in low, medium, and high speed situations. The results of simulations and practical experiments based on the FPGA implemented control system are given to show the performances for wide range speed control. Full article
(This article belongs to the Special Issue High Speed Motors and Drives: Design, Challenges and Applications)
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Open AccessArticle Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets
Received: 1 September 2018 / Revised: 7 October 2018 / Accepted: 18 October 2018 / Published: 1 November 2018
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Abstract
Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics
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Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cyber-Enabled Industrial Systems)
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Open AccessArticle Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research
Received: 9 August 2018 / Revised: 23 October 2018 / Accepted: 24 October 2018 / Published: 1 November 2018
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Abstract
Digitalisation is argued to increase the efficiency of maintenance activities in a production system. One consequence of digitalisation is data deluge; this allows data analytics methods and technologies to be used. However, the actual data analytical methods and technologies used may differ, thus
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Digitalisation is argued to increase the efficiency of maintenance activities in a production system. One consequence of digitalisation is data deluge; this allows data analytics methods and technologies to be used. However, the actual data analytical methods and technologies used may differ, thus leading to many scientific papers on this topic. The purpose of our contribution is to find and cluster scientific papers regarding the implemented approaches relevant for use in production maintenance. Our research is based on a broad, systematic literature review consisting of a two-step search approach combined with additional filtering and classification. Based on the search results, we evaluate and visualise the potential impact of data analytics on the subject of maintenance. The results of this study broadly summarise the research activities in production maintenance, whilst indicating that the impact of data analytics will grow further. Specific methodological approaches are clearly favored. Full article
(This article belongs to the Special Issue Smart Manufacturing, Digital Supply Chains and Industry 4.0)
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Open AccessArticle Using Sensor-Based Quality Data in Automotive Supply Chains
Received: 30 September 2018 / Revised: 19 October 2018 / Accepted: 26 October 2018 / Published: 1 November 2018
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Abstract
In many current supply chains, transport processes are not yet being monitored concerning how they influence product quality. Sensor technologies combined with telematics and digital services allow for collecting environmental data to supervise these processes in near real-time. This article outlines an approach
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In many current supply chains, transport processes are not yet being monitored concerning how they influence product quality. Sensor technologies combined with telematics and digital services allow for collecting environmental data to supervise these processes in near real-time. This article outlines an approach for integrating sensor-based quality data into supply chain event management (SCEM). The article describes relationships between environmental conditions and quality defects of automotive products and their mutual relations to sensor data. A discrete-event simulation shows that the use of sensor data in an event-driven control of material flows can keep inventory levels more stable. In conclusion, sensor data can improve quality monitoring in transport processes within automotive supply chains. Full article
(This article belongs to the Special Issue Smart Manufacturing, Digital Supply Chains and Industry 4.0)
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Open AccessArticle Experimental and Numerical Analysis of the Dynamical Behavior of a Small Horizontal-Axis Wind Turbine under Unsteady Conditions: Part I
Received: 25 September 2018 / Revised: 24 October 2018 / Accepted: 25 October 2018 / Published: 30 October 2018
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Abstract
An efficient and reliable exploitation of small horizontal-axis wind turbines (HAWT) is a complex task: these kinds of devices actually modulate strongly variable loads with rotational speeds of the order of hundreds of revolutions per minute. The complex flow conditions to which small
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An efficient and reliable exploitation of small horizontal-axis wind turbines (HAWT) is a complex task: these kinds of devices actually modulate strongly variable loads with rotational speeds of the order of hundreds of revolutions per minute. The complex flow conditions to which small HAWTs are subjected in urban environments (sudden wind direction changes, considerable turbulence intensity, gusts) make it very difficult for the wind turbine control system to optimally balance the power and the load. For these reasons, it is important to comprehend and characterize the behavior of small HAWTs under unsteady conditions. On these grounds, this work is devoted to the formulation and realization of controlled unsteady test conditions for small HAWTs in the wind tunnel. The selected test case is a HAWT having 3 kW of maximum power and 2 m of rotor diameter: in this work, this device is subjected to oscillating wind time series, with a custom period. The experimental analysis allows therefore to characterize how unsteadiness is amplified moving from the primary resource (the wind) through the rotor revolutions per minute to final output (the power), in terms of delay and amplitude magnification. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software. The comparison between experiments and numerical model supports the fact that the fast transitions are mainly governed by the aerodynamic and mechanical parameters: therefore, the aeroelastic modeling of a small HAWT can be useful in the developing phase to select appropriately the design and the control system set up. Full article
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Open AccessArticle Analysis of Vibration Plate Cracking Based on Working Stress
Received: 7 September 2018 / Revised: 9 October 2018 / Accepted: 10 October 2018 / Published: 26 October 2018
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Abstract
At present, vibroseis has become the major technique to achieve environmental protection and high efficiency in fossil fuel exploration. During such exploration, a vibrator transmits seismic waves to the surface. The waves are excited by continuously changing the load stress from the burden
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At present, vibroseis has become the major technique to achieve environmental protection and high efficiency in fossil fuel exploration. During such exploration, a vibrator transmits seismic waves to the surface. The waves are excited by continuously changing the load stress from the burden of weight of the vehicle and the vibrator’s variable frequency load. This paper will apply a numerical simulation method to develop research on the analysis of vibration plate cracking based on working stress. Based on the structure and mechanism of vibroseis vibrator plate, a vibrator simulation model is built under system dynamics to develop research on the vibroseis plate load stress feature and gain distribution, and change pattern of the plate load stress. The results show that stress response around the upright welding of is high, and there is evident distortion in plate area, which matches the actual fracture position on the plate, and can be confirmed as a key area of plate fatigue. Full article
(This article belongs to the Special Issue Smart Manufacturing)
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Open AccessArticle A Reliability-Centered Maintenance Study for an Individual Section-Forming Machine
Received: 7 September 2018 / Revised: 11 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
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Abstract
This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was
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This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was used as the case study for an automated production system. The machine parts and the working mechanisms were analysed with a special focus on methods of processes and procedures. This will enable the ISM maintenance department to run more effectively and achieve its essential goal of ensuring effective machine operation and reduction in machine downtime. In this work, information is provided on the steps and procedures to identify critical components of the ISM using failure modes and effect analysis (FMEA) as a tool to come up with an optimal and efficient maintenance program using the reliability data of the equipment’s functional components. A relationship between the failure rate of the machine components and the maintenance costs was established such that using the recommended PM program demonstrates evidence of an improvement in the machine’s availability, safety, and cost-effectiveness and will result in an increase in the company’s profit margin. Full article
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Open AccessArticle Nonlinear Model Predictive Control Using Robust Fixed Point Transformation-Based Phenomena for Controlling Tumor Growth
Received: 31 August 2018 / Revised: 9 October 2018 / Accepted: 17 October 2018 / Published: 25 October 2018
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Abstract
In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative
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In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative effect of inherent model uncertainties and measurement disturbances. The results are validated by extensive simulation on the proposed control algorithm from which conclusions were drawn. Full article
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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Open AccessFeature PaperArticle Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings
Received: 15 September 2018 / Revised: 15 October 2018 / Accepted: 17 October 2018 / Published: 18 October 2018
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Abstract
In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific
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In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions. Full article
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Open AccessArticle Mathematical Model of New Type of Train Buffer Made of Polymer Absorber—Determination of Dynamic Impact Curve for Different Temperatures
Received: 13 August 2018 / Revised: 10 October 2018 / Accepted: 11 October 2018 / Published: 18 October 2018
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Abstract
Previous experimental knowledge has confirmed that one of the most influential factors affecting the performance of polymer friction absorbers embedded in buffer housing as part of the buffer and chain coupler is the temperature. This paper defines a mathematical model of a friction-type
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Previous experimental knowledge has confirmed that one of the most influential factors affecting the performance of polymer friction absorbers embedded in buffer housing as part of the buffer and chain coupler is the temperature. This paper defines a mathematical model of a friction-type polymer absorber, PMKP-110. The presented mathematical model specifically includes the influence of the environment temperature on the dynamic impact curve for −60 °C and 15 °C. The dependence between the initial pre-tension of the buffer and the ambient temperature is calculated. The model involves an equation of motion for moving parts of the absorber, and the solution of the differential equation is achieved in Matlab. Results are given as diagrams of the impact deformation and impact speed of the polymer block, with assumed zero initial impact speed. The model can be used to analyze the action of the longitudinal forces that occur during transient conditions of the movement of the carriages. Full article
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Open AccessArticle Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras
Received: 4 August 2018 / Revised: 12 September 2018 / Accepted: 28 September 2018 / Published: 3 October 2018
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Abstract
Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and
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Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms. Full article
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Open AccessArticle Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems
Received: 31 August 2018 / Revised: 17 September 2018 / Accepted: 26 September 2018 / Published: 2 October 2018
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Abstract
The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is
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The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cyber-Enabled Industrial Systems)
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Open AccessArticle Design of Delivery Valve for Hydraulic Pumps
Received: 17 May 2018 / Revised: 4 September 2018 / Accepted: 5 September 2018 / Published: 1 October 2018
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Abstract
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
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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. Full article
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Open AccessArticle Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines
Received: 18 August 2018 / Revised: 19 September 2018 / Accepted: 20 September 2018 / Published: 1 October 2018
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Abstract
In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed
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In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction. Full article
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