Smart dampers-based vibration control – Part 1: Measurement data processing. (November 2020)

• Record Type: Journal Article
• Authors: Nguyen, Sy Dzung; Choi, Seung-Bok; Kim, Joo-Hyung
• Is Part Of: Mechanical systems and signal processing. Volume 145(2020)
• Publication Date: 2020-11
• Subjects: AfODST Algorithm for determining the ODST -- ANFIS Adaptive neuro-fuzzy inference system -- CDS Cluster data space -- CoFilter Combined filter -- I-MRD Inverse magnetorheological damper model -- ISSmDCS Integrated structure–SmD control systems -- I-SmD Inverse smart damper model -- IDS Initial data space -- LMSM Least mean squares method -- LVDT Linear variable differential transformer -- MND Massive and noisy measuring database -- MD Measured data -- MRD Magnetorheological damper -- ODST Optimal data screening threshold -- ODSTbF ODST based filter -- RMSE Root-mean-square error -- SmD Smart damper; Data screening threshold -- Impulse noise filtering -- ANFIS-based filtering -- Optima data screening threshold; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106958
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A new algorithm for determining an optimal data screening threshold (ODST) is presented. ODST-based filter and combined filter are proposed. The filters can deal well with random and impulse noise, the combined filter can be also used for white noise. Abstract: Exploiting smart dampers (SmDs) based on data-driven models have been seen as an appropriate approach for many applications such as vehicle suspension system. Reality has shown that the error of SmDs' identification due to noise in the measured data (MD) sets as well as uncertainty related to the mathematical tools selected to describe control systems reduces control efficiency. To overcome this issue we are interested in finding effective solutions for online filtering noise in MD, selecting and building data-driven models of SmDs, and seeking an appropriate approach to reduce the model errors. To undertake these, we divide the research into two parts; part 1 and part 2. In this current part, we focus on the filtering of the noise by proposing two new filters. Deriving from a discovered optimal data screening threshold (ODST), the first one is an ODST-based filter (ODSTbF) for dealing with random and impulse noise (IN). The second one named combined filter (CoFilter) is a combination of the ODSTbF and the median smoother to extend the filtering capability. To determine the ODST of a data source, a new algorithm for estimating the ODST named AfODST is proposed via an offline process. Many surveys using MD coming from a magnetorheological damper (MRD) are performed to evaluate positive effects of the proposed method.