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Machines, Volume 6, Issue 4 (December 2018)

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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
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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