A Real-Time Positioning System of Manufacturing Carriers Deploying Wireless MEMS Accelerometers and Gyroscopes

Modern manufacturing systems face ever-increasing pressure to maximize efficiency of production processes, minimize downtime due to unexpected deviations from normal operation, and maintain agility in dynamic market conditions. Detailed, real-time asset tracking is essential for achieving these goals. Pallets are widely-used for transporting raw materials, intermediate products, and final products in automated assembly and manufacturing lines. A sophisticated pallet monitoring system can provide possibilities for optimizing pallet routing in real time, enable dynamic scheduling changes, and historical traceability required for error diagnosis and repair. Traditionally, pallets are monitored by networks of sensors, such as RFID readers or proximity sensors to collect location data. These sensor networks are rarely dense enough to provide precise continuous data about pallet location. Real-time pallet tracking data is thus limited to recording timestamps at static checkpoints. This thesis presents an asset-aware management tool for continuous pallet location monitoring based on event logs obtained from intelligent wireless devices embedded in each pallet. Each wireless device, equipped with a 3-axis accelerometer and a 3-axis gyroscope, provides accurate information about pallet movement. The raw sensor data is pre-processed into an event stream, which is sent to a server over a 6LoWPAN network. The software developed in this research implements an algorithm for processing event logs to determine exact pallet location using artificial intelligence techniques. Calculated pallet position can be provided to high-level enterprise systems, and to manufacturing execution systems for use in scheduling, routing, and visualization of the production line. Designing the SCADA system was also part of this thesis. The solution was successfully deployed in the FASTory, a 12-cell light assembly line in the Factory Automation Systems and Technologies Laboratory (FAST-lab.) at Tampere University of Technology, as part of eSONIA, a European Commission-cofunded research project on using service-enabled embedded devices for realizing an asset-aware, self-recovering plant. The proposed solution demonstrates a novel approach for continuous, real-time pallet location tracking based on wireless sensors