Implementation of BCI based semi-automated impact device for performing Impact Synchronous Modal Analysis

Abstract

Current Impact Synchronous Modal Analysis (ISMA), an operational modal analysis technique incorporated with either manual impact hammer or Automated Phase Controlled Impact Device (APCID) faces challenges on its effectiveness and practicality in real industrial applications. This study utilizes a Brain Computer Interface (BCI) based portable semi-automated impact device with the aim to solve the current issues of ISMA and complement both the manual impact hammer and APCID. A low cost, portable and wireless Electroencephalogram (EEG) device is used with machine learning to predict impact time before the impact using manual impacts and integrated with APCID control to compensate for the behaviour randomness. This semi-automated impact device, also known as EEG-ISMA, provides an accurate, practical, time- and cost-effective solution in performing ISMA during operation with low mean error in impact time prediction, superior cyclic load component suppression capability, accurate modal parameters extraction and shorter time required.