# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Case 1: Bearing Lifetime Estimation  
Bearing lifetime estimation case consists of a method and a proof-of-concept implementation for the closed-loop design of crane bearings by performing automated data analysis on crane usage data and bringing the analysis to a system used daily by product developers. The majority of the results described in this subsection were presented earlier in a project seminar [51] by [PERSON] and [PERSON]. A detailed description of the data analysis and visualization was given in a bachelor's thesis by [PERSON] [52]. The data flow for the case travels through several technical tools: (i) the physical crane with sensors, Programmable Logic Controller, and an OPC UA server, (ii) physical IoT gateway, (iii) cloud-based IoT platform with data storage, custom engineering formulas, and web technologies (e.g., JavaScript), and (iv) a PLM system. The crane produces the raw data and provides an OPC UA interface for the IoT gateway. The gateway sends selected usage data to an IoT platform that stores the data, performs customized analysis (kinematics-based virtual sensoring and bearing lifetime calculations) on the data, as well as hosts a visualization that is displayed on a PLM system. The data flow is shown in Figure 5.  
Figure 4: Digital twin-based product lifecycle management from product idea to product in use. The accumulating knowledge is used to develop and improve the product and to learn how to make future products better [49]. Collected raw data included the vertical position and the load of the crane, which were further refined with kinematic equations to determine the revolutions and load inflicted on each rope sheave bearing. The crane has three rope sheaves, each with a distinct number of revolutions. During the project, data collection for this implementation was straightforward with existing industrial tools, but presenting meaningful data in a meaningful way proved to be challenging. Methodology for this case includes engineering design knowledge and practical data flow implementation tools from raw data generation to end-user visualization system. According to basic engineering dimensioning formulas, the lifetime estimations of ball bearings are based on the usage frequency and stress. When we get these data from the crane, we can compare the designed lifetime and the usage-based predicted lifetime of the component. For the example crane Ilmatar, the usage-based lifetime prediction for the bearings was approximately a thousand years due to the low usage frequency and light loading exerted on the crane used for research and education. With the lifetime prediction, a machine designer can determine if the component has been selected correctly. This knowledge can be leveraged to design new cranes that better fit their expected usage profile. Of course, the machine designer can perform estimations themselves, but the automated analysis saves their time for more demanding tasks and with large fleets, manual estimations may be infeasible. As future development, automated analysis can be used for triggering alerts if a crane is used more than expected. The end-user interface of the case is the visualization that was shown inside a web-based PLM application (Figure 6a). Figure 6b shows the estimated usage (green line) of the bearing if the use of the crane continues similarly as during a selected time period (red curve). The time period and other parameters of the graph, such as the designed lifetime (pink line) that is used for drawing the reference line (blue line), can be changed either by changing parameters in the URL of the browser or with a configurator user interface shown in Figure 6c. Figure 5: The data flow of the bearing lifetime estimation case. Designing a new crane is the purpose of providing the data, but not implemented during this study.