Identification of modal parameters from noisy transient response signals

In the process of impact testing of large-scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal-to-noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this problem, the mean averaging of repeatedly measured frequency response function estimates is often employed in practical applications. However, a large number of impact tests are not practical for the modal testing of large-scale mechanical equipment. The primary objective of this paper is to reduce the averaging operation and improve the accuracy of the modal identification by using a noise removal technique. A hybrid denoising method is proposed by combining the Wiener and improved minimum mean-square-error short-time spectral amplitude estimators. The proposed method can effectively remove both stationary and highly nonstationary noise while preserving the important features of the true forced response signals. The simulation results show that the proposed noise removal technique improves the accuracy of the estimated modal parameters using only one impulse response signal. The experimental results show that the proposed method can accurately identify a natural frequency that is very close to a strong interference frequency in the modal test of a 600-MW generator casing.