Mass Inertia Effect Based Vibration Control Systems for Civil Engineering Structure and Infrastructure

This chapter introduces some recent research works carried out in the Blast Resistance and Protective Engineering laboratory of Harbin Institute of Technology (HIT-BRPE) during the past few years. The EMD control system is shown to be effective and feasible for vibration control of civil engineering structures subjected to, such as earthquake, excitations. The DDVC based AMD control system is suitable for low frequency vibration and motion control. The innovative passive TRID system is applicable for rotation and swing motion control, whereas linear TMD system is shown to be invalid for structural swinging motion. All of the control systems mentioned in this chapter, whatever active or passive or hybrid, have a common characteristic, which is to utilize the mass inertia effect either to provide counter force support for functioning of actuator, e.g. AMD subsystem, or to provide gyrus or rotary inertia for anti-swinging motion of suspended structure. Traditionally, these systems have been called Active Mass Damper/Driver (AMD) or Tuned Mass Damper (TMD), herein we want to emphasize the mass inertia effect and its functions. The basic is to be a necessary component of a control system, and more important is its way of working in the subsystem

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately