The technology of swarm intelligence has been applied to a mechanical vibration monitoring system composed of a network of units equipped with sensors and actuators. The expression of “swarm intelligence” was first used in 1988 in the context of cellular robotic systems, where lots of simple agents may generate self-organized patterns through mutual interactions. There are various examples of the swarm intelligence in the natural environment, a swarm of ants, birds or fish. In this sense, the network of agents in a swarm may have some kind of intelligence or higher function than those appeared in a simple agent, which is defined as the swarm intelligence. The concept of swarm intelligence may be applied in diverse engineering fields such as flexible pattern recognition, adaptive control system, or intelligent monitoring system, because some kind of intelligence may emerge on the network without any special control system. In this study, a simulation model of a five degree-of-freedom lumped mass-spring system was prepared as an example of a mechanical dynamic system. Five units composed of a displacement sensor and a variable damper as actuator were assumed to be placed on each mass of the system. Each unit was connected to each other to exchange the information of state variables measured by sensors on each unit. Because the network of units configured as a mutual connected neural network, a kind of artificial intelligence, the network of units may memorize the several expected vibration-controlled patterns and may produce the signal to the actuators on the unit to reduce the vibration of target system. The simulation results showed that the excited vibration was reduced autonomously by selecting the position where the damping should be applied.
- Aerospace Division
Application of Swarm Intelligence to a Vibration Monitoring System
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Hiura, T, & Morishita, S. "Application of Swarm Intelligence to a Vibration Monitoring System." Proceedings of the ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation; Structural Health Monitoring. Snowbird, Utah, USA. September 18–20, 2017. V002T03A004. ASME. https://doi.org/10.1115/SMASIS2017-3734
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