Low Cost Ambient Vibration

Low-cost ambient vibration analysis focuses on extracting meaningful information from readily available vibrational data, often using inexpensive sensors, to monitor various systems and structures. Current research emphasizes the development and application of machine learning models, including deep neural networks (like transformers and convolutional neural networks), and explainable AI techniques to improve prediction accuracy and interpretability of vibration data for applications such as structural health monitoring, fault detection in machinery, and robot control. This approach offers significant potential for cost-effective and efficient monitoring and predictive maintenance across diverse fields, ranging from civil engineering to robotics and manufacturing.