Abstract
Operational modal analysis (OMA) has recently been applied to the condition monitoring of rotating machinery. Conventional OMA is based on a one-dimensional signal. The natural modes of a rotating machinery can be classified into two types: a forward whirling mode and a backward whirling mode. Although the magnitudes of the natural frequencies of these two modes are separated by the gyroscopic effect, they can become close to each other and difficult to distinguish. This characteristic may make it difficult to apply conventional OMA based on a one-dimensional signal to rotating machinery. To monitor and diagnose the rotating machinery in the operating condition with high precision, it is necessary to develop the OMA method that can separate this information of the forward and backward whirling modes and can accurately estimate the vibration characteristics of each mode. However, only one previous report has addressed the application of OMA to rotating machinery with the capability of separating whirling direction information, and this approach requires an excitation signal. In this study, a novel OMA method, referred to as full OMA, has been developed, which is capable of separating forward and backward whirling direction information without the need for an excitation signal. To achieve this, signal data in both the x and y directions are acquired, and their autocorrelation and cross-correlation functions are calculated and combined as complex numbers. Spectral analysis of these functions yields a pseudo–full frequency response function (FRF), from which modal parameters for each whirling direction can be estimated. The validity and usefulness of the proposed full OMA method have been confirmed through both theoretical analysis and experimental validation. This full OMA method enables the accurate estimation of vibration characteristics for each whirling direction, even when the forward and backward natural frequencies are in close proximity. Consequently, the proposed full OMA method is highly effective for monitoring and diagnosing rotating machinery.
