Rotor self-excited vibrations due to solid/fluid interaction, such as occurring in seals, can easily be reduced or eliminated by controlling fluid circumferential velocity. This is known as “anti-swirl” technique. An active “anti-swirl” control system for rotating machines is described in the paper. While mainly controlling rotor self-excited vibrations, this active control system reduces also rotor lateral vibrations caused by other factors (such as unbalance), by increasing the system effective damping.

This paper outlines the sweep frequency rotating force perturbation method for identifying the dynamic stiffness characteristics of rotor/bearing/seal systems. Emphasis is placed on nonsynchronous perturbation of rotating shafts in a sequence of constant rotative speeds. In particular, results of the identification of flexible rotor multimode parameters and identification of fluid forces in seals and bearings are given. These results, presented in the direct and quadrature dynamic stiffness formats, permit the separation of components for easy identification. Another example of the perturbation method application is the identification of the lateral–torsional coupling due to shaft anisotropy. Results of laboratory rig experiments, the identification algorithm, and data processing techniques are discussed.

The rotordynamic behavior of an industrial gas turbine rotor train was assessed on site, and the sensitivity to unbalance was quantified. An outline of the measurement procedure is given. Differential data reduction with test unbalances was undertaken to minimize the influence of measurement uncertainty. A test unbalance was installed for one run and then shifted by 180° for the consecutive run. With differential data, the effective dynamic properties of the rotor - support - system can be estimated more accurately. A rotordynamic model was used to identify the support system parameters based on measured data. For the analysis, the anisotropic, elliptical vibration orbits were decomposed into two counter-rotating circular orbits, and the support system parameters identified match the originally predicted values well. The methods of differential data reduction, rotor train mode shape presentation, elliptical orbit decomposition, and the link of measurement to analytical models with parameter definition are described. Examples from on-site measurements are included for illustration.

Currently most rotor diagnostic algorithms focus on extracting as much information as possible from the signal of a single transducer. This limits these algorithms to only processing information about the projection of the rotor motion onto the probe axis. Utilizing single probes is adequate for isotropic systems where the projections are the same for any probe orientation. However, for anisotropic systems the results of the algorithm can be different depending on the orientation of the measurement transducer. This leads to the blind men and elephant conflict, multiple descriptions of the same animal, or in our case rotor motion. For example, if the transducer is located on the minor axis a great running machine, on the major axis a machine that needs balancing. The phase is also inconsistent, rotate the transducer a few degrees and the indicated phase jumps drastically. If the transducer is on the ellipse axis the high spot and heavy spot are together below the resonance, move the probe off the axis and the high spot and heavy spot are no longer related. More sophisticated diagnostic methodologies which correlate data from multiple axial locations, such as relative phase and modeshape, are affected even more than balancing by the phase inconsistency. What is really desired, is information about the actual motion of the shaft in the two dimensional plane. Up until recently the only data presentation format that addressed the planer motion was orbit. Orbits are powerful diagnostic tools, but have the limitation of only showing motion at one speed. What is needed are similar processing techniques that present complete motion over the entire speed range and report the same information for all probe orientations, such as complex variable filtering. This paper presents the concept of complex variable filtering and explores some situations in which it provides superior results to those obtained utilizing conventional single probe techniques.