The average correlation signal based stochastic subspace identification for the online modal analysis of a dump truck frame

This paper presents a new method for the online modal analysis of heavy-duty dump truck frames in order to verify the true performance of the frame. Rather than commonly using raw response signals for covariance-driven stochastic subspace identification (Cov-SSI), it takes the average correlation signal of the raw signals as the input data of Cov-SSI for more efficient online modal identification. In this way, different data records can be combined coherently and the noise content and nonstationary phenomena are suppressed effectively, which allows the effective use of acceleration signals from the frame of the truck running under different road conditions and operating conditions for online modal analysis. It shows the theoretical basis of the proposed method and verifies its performance with both simulated and measured data sets. The results show that the proposed method yields a more accurate results compared with that of conventional Cov-SSI that uses raw signals as the input data. Therefore, the vibration behaviors of the frame obtained online are reliable, realistic and hence valuable for assessing the overall dynamic performance of the vehicle.