Development and implementation of a data collection system for temperature and climatic test systems
Abstract: A large part of developing products is the testing and validation of the new products. This process requires expensive test systems and a great deal of time and space. Because of these factors it is very important to maximize the usage of the test systems. Therefore, a monitoring system has been developed by the test and validation department at BorgWarner Landskrona that can read the operational status of some of their test systems. This system collects the status data via a single-board computer and then logs status changes in a database over the local network. At the end of each day the status data is compiled and the total runtime of each status is displayed in the office. This thesis consists of expanding this monitoring system to include a type of test system called temperature and climatic test systems. These test systems are designed to subject the device under test to very high or very low temperatures and to quickly be able to change between the two. The temperature and climatic test systems have been identified as a bottleneck and therefore BorgWarner wants to gather status data from these test systems to be able to know if more test systems need to be purchased or if a change in usage would alleviate the bottleneck. The data collection system developed during this thesis uses a single-board computer running a Python script. The python script contains the IP-address, port number and alias of the test systems it has been designated. When the script is started it connects to the local database and then gets the latest status for each alias. The script then cycles through the IP-addresses sending commands over the network and the test systems respond with their status. This status is compared to the last saved status and if they are different the new status is logged in the database. Some improvements and changes are to be made but the data collection system works as intended and is in use. More time is needed for the data collection until conclusions can be drawn to ensure that the data is representative of the real-world long-term usage.
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