Robust energy management for IoT machine elements
Abstract: Advancements in semiconductor technology have reached the point where ambient energy surrounding their environment can power sensors and microprocessors. It enables new strategies for energy management which is necessary to continue the sensorization of our environment. With the vast amount of and the rate at which the number of interconnected devices is increasing, there is a need to power resource-constrained devices through other means than disposable batteries. Harvesting ambient energy from the vicinity of the device is one solution.SKF, a producer of rolling element bearings, produces approximately one billion bearings per year. A mechanical part that is essential for rotating machinery and that has the potential to measure and monitor vital parts of the machine. A scenario in which bearings are embedded with electronics to monitor process and health parameters that can be analyzed on site and collected to remote locations is a crucial motivator for this thesis.This thesis presents some of the most common sources of energy used for harvesting energy in rotating environments and discusses how different transduction methods can convert ambient energy into electrical energy. General solutions that robustly apply to many applications are of great interest. The investigated technologies should apply to a dirty and encapsulated industrial environment; therefore, certain energy sources, for example, sunlight and radio frequencies, are omitted. Vibration harvesters are investigated and modeled in SPICE to verify performance gains using a novel circuit for non-linear power extraction for piezoelectric materials. Simulations showed that a weak coupling from the electrical system to the mechanical system would greatly benefit non-linear extraction techniques. Such a weakly coupled system can be created in a bearing. Mechanical load and rotation generate cyclic strain in the bearings raceway; the cyclic strain can be utilized by applying piezoelectric patches to the raceway to power embedded systems and sensory information from the piezoelectric patch can also be used to monitor the bearing. Finally, trends and limits for computation, communication, data acquisition, and energy storage are investigated to evaluate the most robust energy storage solution for future embedded systems, which are powered by an energy harvester.
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