Review of the Tuned Mass Damper Inerter(TMDI)in Energy Harvesting and Vibration Control:Designs,Analysis and Applications

Tuned mass damper inerter(TMDI)is a device that couples traditional tuned mass dampers(TMD)with an inertial device.The inertial device produces resistance proportional to the relative acceleration at its two ends through its“inertial”constant.Due to its unique mechanical properties,TMDI has received widespread attention and application in the past twenty years.As different configurations are required in different practical situations,TMDI is still active in the research on vibration control and energy harvesting in structures.This paper provides a comprehensive review of the research status of TMDI.This work first examines the generation and important vibration control characteristics of TMDI.Then,the energy harvesting performance of electromagnetic tuned mass damper inerter(EM-TMDI)is discussed.This work emphasizes the formation of a passive dynamic vibration absorber by coupling traditional TMD with inertial devices.This paper also summarizes the design and implementation of optimal vibration control and energy harvesting for TMDI.Furthermore,this paper details the applications of TMDI in the fields of bridges and building engineering.Finally,this paper summarizes the necessity of research on tuned mass-damper-inertia,the challenges faced currently,and future research directions,such as control of parameters in electromagnetic energy harvesting TMDI systems and low-cost TMDI.

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.