Use-Case ISP. 1-1.2 Data stream computation of data streams that have previously been stored in file sets (Use Case provided by Hägglunds Drives and Volvo CE)
Use-Case ISP. 2-3: Real-time data streaming and computation from actual sensor in construction equipment (Use case provided by Volvo Construction Equipment) Volvo CE has a vision that customer support sells services to customers where the machines are monitored in more detail. In this project, the information needed by such a support is realized for one or more machines. Upon alarms, immediate and corrective actions are then taken to avoid failures or updates maintenance schedules if there is a perceived increase regarding for instance the level of failure risk. The streaming data from the machines can then be compared to find machines behaving differently to the rest of the complete Volvo CE customer base machine fleet. This is an approach that reduces the need for diagnoses based on detailed knowledge of the machine behavior, which in practice often cannot be realized due to resource needs and the complexity in modeling material imperfections etc. since not only electronics fail but also mechanics. The alternatives to this so-called model based diagnosis are rule-based and case-based reasoning. Rule-based reasoning uses a set of fixed rules set up to find every possible error based on expert knowledge. Case based reasoning mimics the human mind where similar historical cases are compared with to find the one with the closest match. Apart from simple comparison of trends and absolute values with compensation of operator and application characteristics, case-based reasoning could be employed in this project to compare events among the various machines using data from previous failures among the machines. If the data is real-time, corrective actions can be undertaken using short term trends early and fast enough to avoid also some the random failures that cannot be predicted from long term trends. Customer support would in this vision have a central monitoring function with a number of operators surveying the connected machines, whose volume could exceed 10 000. Similar requirements can be imagined in nuclear power or large chemical plant surveillance. The number of operators should of course be kept low which means that a large amount of information needs to be condensed into decision information to be presented to the operators. If an operator finds a machine of particular interest, the operator could be given the option to get more detailed information. The latter mechanism and the remains of this paragraph are implemented in this project. The operator could then dema...
Use-Case ISP. 2-1: Real-time data streaming and computation from actual sensor data in hydraulic systems (Use case provided by Hägglunds Drives) 38 5.2.1.1 Scenario 40 5.2.1.2 Raw data 41 5.2.1.3 Metadata 42 5.2.1.4 Derived data 43 5.2.1.5 Validation 43 5.2.1.6 Action and queries 44
Use-Case ISP. 2-2: Real-time data streaming and computation from actual sensor at milling tools (Use case provided by Sandvik Coromant) 45 5.2.2.1 Scenario 46 5.2.2.2 Raw data 47 5.2.2.3 Metadata 49 5.2.2.4 Derived data 50 5.2.2.5 Validation 50 5.2.2.6 Action 51
Use-Case ISP. 2-3: Real-time data streaming and computation from actual sensor in construction equipment (Use case provided by Volvo Construction Equipment) 52 5.2.3.1 Scenario 56 5.2.3.2 Raw data 58 5.2.3.3 Metadata 60 5.2.3.4 Derived data 61 5.2.3.5 Validation 61 5.2.3.6 Action 62 5.2.4 Use-case Background ISP-2-4: Customer usage modelling. (Background provided By Volvo CE) .. ........................................................................................................................................................ 62 5.3 ISP-3 DATA STREAMS WITHIN COLLABORATIVE ENVIRONMENTS 65
Use-Case ISP. 1.1 Data streams derived from complex simulations in the virtual design process 14
Use-Case ISP. 1.1 Data streams derived from complex simulations in the virtual design process The use case works with the following elements (highlighted) of the DSDM schema:
Use-Case ISP. 1-1.2 Data stream computation of data streams that have previously been stored in file sets (Use Case provided by Hägglunds Drives and Volvo CE) MetaData OperationalData StoredData
Use-Case ISP. 2-1: Real-time data streaming and computation from actual sensor data in hydraulic systems (Use case provided by Hägglunds Drives) MetaData OperationalData StoredData The most important factors for Hägglunds drive systems are high availability with no unplanned stops and best possible Total Cost of Ownership (TCO) for the customer. An important factor to improve the TCO is to increase the energy efficiency by optimization of the operating point. However this means higher usage of the system components where Hägglunds Drives needs the online monitoring tools to follow the system behavior to keep the high reliability and to improve the maintenance possibilities for increased system availability. DataStream DataSource SimulationStream ValidationStream RawDataStream DerivedStream CollaborationStream StoredStream
Use-Case ISP. 2-2: Real-time data streaming and computation from actual sensor at milling tools (Use case provided by Sandvik Coromant) There are many factors and dependencies in machining that must be considered when developing efficient sustainable machining solutions. In order to develop, validate and provide functional products within this area, all parts in the machining system (including cutting tools, machine tools, part to be machined, materials, controller, machining strategy, cutting data etc.) need to be considered in a comprehensive way. Consequently, data parameters from all these different but interlinked parts need to be captured, combined, and analysed. The complexity is high due to very large amounts of data (static and dynamic), and that these data originate from many different sources such as: PLM systems (product data of components to be machined) Manufacturing resource management systems (data about cutting tools, machine tools etc.) CAM, process planning systems (machining strategies, cutting data, clamping methods etc.) Material databases (material data) Sensors (in-process monitoring data of the machining process) Controllers (in-process data such as axis positions, current power values etc. parameters for productivity analyses etc.) Metrology and quality evaluation (critical dimensions, tool wear parameters etc.). Data stream analysis Partners etc. Control data Sensor data Process data Machine Tool (lab) Control data Sensor data Process data Machine Tool (prod) CNC controller
