A tracking system tracks the position and movement of users as well as objects and links the augmented content to real elements. For the interaction with the above mentioned computer system, input devices such as gloves, tablets or PDAs deliver required data. Due to the advantages of user-friendly consumer hardware  and their vast acceptance  we focus on smart devices, which also combine the components stated before into a single device. HMDs might offer advantages over in the field of visualization , but lack the maturity level of industrial tablet PCs.
According to Wang et al. Approaches utilizing AR in a collaborative manner such as ,  and  only to some extend address the elements of collaboration defined in  and previously described in the introductory section. Especially recurring high-value collaboration processes, on which we focus in this approach, require well-structured and easily executable activities performed by the practitioners .
In addition to the requirements regarding the AR system and the collaboration among the related actors, the collaborative maintenance process itself and the information retrieval need to be considered in our approach. It needs to support the core elements of collaboration, i. To that extend the concepts published in  and  will be partly included into the approach for maintenance support for multiple technicians on-site.
The approach at hand therefore focuses on the support system onsite rather than on the early phases of maintenance planning. Concept Haberfellner and Daenzer  cite that models as a depiction of reality must be convincing enough in regards to the situation and problem position. So the question of functionality and problem relevance is to be asked when developing a model.
PriMa: a prescriptive maintenance model for cyber-physical production systems
Harrison  states that the strength of graph databases lies especially on the use cases in which the correlation of elements is more important than the elements themselves. After the consideration of the preceding section and based on the graph-based model the authors described in , the changes in the given situation made it necessary to implement adaptations to the architecture. The considered processes in this concept include the creation of tasks to fulfill goals, the task decomposition into smaller activities to fulfill the whole task, the assignment of technicians to said activities and the actual on-site collaboration and individual maintenance assistance.
It should be clarified that the functionalities of this approach are based on the considerations of Harrison, Haberfellner and Daenzer to find out which things are relevant to the given activities. Furthermore, it is necessary to focus on connections that exist between those things and the rules that bind things Figure 2: Graph representation of the described problem and connections together.
In regard to Harrison  things in the real world can be displayed as knots in a graph. Connections between the individual things can be depicted with edges.
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Constraints ensure the necessary rules for validity of connections so that only certain types of knots may have certain types of in- and outgoing edges. According to Locke  a goal is a desired result or a desired status, so in the context of maintenance the general goal is the functionally desired status of the concerned machine. Goals must be specific with precise instructions and desired status x Measurable. Goals must be measurable to judge the completion of a goal x Accepted.
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Goals must be accepted by the participants in regard of goal attainment x Realistic. Goals must be realistic so they can actually be completed x Terminated. To enable the management of tasks, facilitators and practitioners for individual assignments, Figure 3 shows the created entities and their relations that can be managed in the graph-based model. In the following the concept will be made clear based on selected parts of the six-step concept for collaborative engineering  and the functionalities of our proven approach.
As mentioned in the aims and requirements section the approach at hand will show potential for newly designed techniques bottom-up, starting in the operational phase.
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The focus lies strictly on recurring high-value collaboration, which 21 Michael Abramovici et al. The infrastructure of the approach is depicted in Figure 1. Every machine that is to be serviced has to be part of the Internet of Things for the planning, tracking and execution of the collaborative work processes, whereas technicians are to be equipped with smart devices with an internet connection to make use of real-time synchronization of data between all concerned IoT entities. The same goes for the status of valves in the follow-up task.
Only for non-closed valves would an instruction be generated. The collaborative aspect adds another level of complexity to the maintenance support assistant.
Generally, there are different views on the data in the graph-based model. The first is the administrative or master view on the model.
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Without restrictions one is able to see the progress of each technician in every activity he is involved in. Furthermore, the complete status of the machine on-site is visible given a feasible type of visualization. The view of each technician is not as holistic as the so-called master view, because is neither necessary nor helpful.
The technicians view is focused on the current activity to establish the desired state, which can be delayed or even interrupted by actions of other technicians or system failures and their resulting warnings. A Task is a distinct action to be fulfilled and coordinated, while an Activity is a specific, measurable, realistic action that is specifically assigned to a Technician in order to be executed. To be measurable the graph-based model connects the individual desired status to the concerning activity, giving the algorithms a specific value to calculate with.
These functionalities are shown in Figure 2. These activities are connected to both a specific technician as well as the desired result of the activity. Results are not stored in an isolated matter, but in relation to the concerning sensor device, which is mounted to a specific part of the machine at hand. No work instruction would be created if the desired status of an activity is met before the technician starts working.
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For this matter sensory data is captured and processed automatically and manual inputs can be set where sensors are not applicable, i. For the automatic generation of the graphical user interface GUI dialogs to capture non-sensor inputs, the possible states must be defined beforehand. For that matter a non-sensor kind of knot for the graph-based model is needed that can take 22 Michael Abramovici et al.
Figure 4 shows the implementation of this pattern. The manual input class knots offer the same functionality as sensor class knots, with the exception of the missing automatic retrieval of data. As far as communication is concerned, the concept offers a general functionality to send a warning or call a colleague to take a look at the current working. While waiting for the completion of a relevant activity, the current progress of said activities is shown to the waiting technician.
Prototype implementation and validation To validate the capabilities of the presented approach a prototypical implementation was conducted with the test system being the hydraulic system used in . We modelled a task as depicted in Figure 5 to coordinate two technicians and attached the desired values and dependencies accordingly like shown in Figure 2. In our validation scenario John Doe is in front of a hydraulic system, Laura Ipsum is should be on the backside of the machine.
A screenshot from the maintenance assistance application can be seen in Figure 6, in the situation when Laura Ipsum is waiting for the completion of the two tasks of John Doe. Figure 6: Screenshot of the prototypical application 5. Conclusion The authors have shown that the approach at hand supports collaboration in a laboratory environment.
Using the cloud-side dynamic data model created for single-user support and enhancing its capabilities to distribute collaboration-relevant messages to multiple users, the smart device based assistant system is able to support groups of technicians coordinating their work. Future work will focus on testing the approach with both a more complex system and more complex tasks.
The approach will be enhanced in regard of the planning process of collaborative engineering in order to establish a holistic framework for collaborative engineering processes by means of Augmented Reality. After the second laboratory experiment our goal is a real-world validation on an industrial site. Genkova, Petia, Ringeisen, Tobias Hrsg. Karampelas, Panagiotis, Bourlai, Thirimachos Eds. November 18, Series : Progress in IS.
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