UWB Positioning for Industrial Applications: the Galvanic Plating Case Study

2019 ◽  
Author(s):  
Alessio Martinelli ◽  
Sara Jayousi ◽  
Stefano Caputo ◽  
Lorenzo Mucchi
Keyword(s):  
Industrial Applications

Bluetooth: A Case Study for Industrial Applications

2008 ◽  
pp. 1-5
Author(s):  
D.M. Akbar Hussain ◽  
A.A. Tabassam ◽  
M. Zafarullah Khan ◽  
Shaiq A. Haq ◽  
Zaki Ahmed
Keyword(s):  
Industrial Applications

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

An Optimization Method of Heat Exchanger Network for Non-Fixed Investment Costs

2012 ◽  
Vol 516-517 ◽  
pp. 135-139
Author(s):  
Xiang Bai Hu ◽  
Guo Min Cui ◽  
Hai Zhu Xu ◽  
Jin Yang Wang
Keyword(s):  
Heat Exchanger ◽  
Local Minimum ◽  
Optimization Method ◽  
Industrial Applications ◽  
Optimization Process ◽  
Fixed Investment ◽  
Heat Exchanger Network ◽  
Innovative Method ◽  
Minimum Solution

In order to overcome the difficulty of easily falling into the local minimum solution during the optimization process of heat exchanger network which is not considered fixed investment costs, an innovative method was presented. The total areas of local minimum solution were distributed equally, and then the distributed areas were assigned to initial areas for further optimization. The better local minimum solution was sought out after jumping out of local minimum solution. Through some case study, it presents that this optimization method is able to obtain better optimization results which is more suitable to industrial applications.

Algorithmic path planning of static robots in three dimensions using configuration space metrics

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 295-315 ◽  
Author(s):  
Debanik Roy
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Heuristic Algorithm ◽  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Three Dimensions ◽  
Task Space ◽  
Articulated Robot

SUMMARYCollision-free path planning for static robots is a demanding manifold of contemporary robotics research, vastly due to the growing industrial applications. In this paper, a novel ‘visibility map’-based heuristic algorithm is used to generate near-optimal safe path for a three-dimensional congested robot workspace. The final path is obtainable in terms of joint configurations, by considering the Configuration Space of the task space. The developed algorithm has been verified initially by considering representative 2D workspaces, cluttered with different obstacles with regular geometries and then after with the spatial endeavour. A case study reveals the effectiveness of the developed modules of the configuration space mapping, pertaining to a five degrees-of-freedom low payload articulated robot.

scholarly journals Analysis of Fault-Tolerant Operation Capabilities of an Isolated Bidirectional Current-Source DC–DC Converter

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3203
Author(s):  
Andrei Blinov ◽  
Roman Kosenko ◽  
Andrii Chub ◽  
Volodymyr Ivakhno
Keyword(s):  
Failure Modes ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Current Source ◽  
Industrial Applications ◽  
Operating Range ◽  
And Performance ◽  
Multi Mode ◽  
Short Circuit Condition

Reliable and predictable operation of power electronics is of increasing importance due to continuously growing penetration of such systems in industrial applications. This article focuses on the fault-tolerant operation of the bidirectional secondary-modulated current-source DC–DC converter. The study analyzes possible topology reconfigurations in case an open- or short-circuit condition occurs in one of the semiconductor devices. In addition, multi-mode operation based on topology-morphing is evaluated to extend the operating range of the case study topology. The influence of post-failure modes on the functionality and performance is analyzed with a 300 W converter prototype. It is demonstrated that failure of one transistor in the current-source side can be mitigated without dramatic loss in the efficiency at maximum power, while preserving bidirectional operation capability.

scholarly journals Predicting maintenance work hours in maintenance planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waqas Khalid ◽  
Simon Holst Albrechtsen ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Industrial Applications ◽  
Research Literature ◽  
Work Hours ◽  
Machine Learning Algorithms ◽  
Maintenance Planning ◽  
Planning And Scheduling ◽  
Content Type ◽  
Work Hour ◽  
Maintenance Work

PurposeCurrent industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.Design/methodology/approachThe paper uses the design research methodology utilizing a case study to validate the proposed method.FindingsThe case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.Practical implicationsThe study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.Originality/valueThe proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

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