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The effectiveness of common algorithms for feedforward compensation of narrowband disturbance depends mainly on the model quality. To avoid this dependency several direct adaptive control algorithms without explicitly identified secondary path models have been developed over the last years. However an overview of their properties and a comparison of their performances in a standardized benchmark is still lacking. In this paper the three most promising algorithms are modified for narrowband feedforward vibration control for the use in rotating machinery. As in this application the reference signal is generated using the frequency measurement from a speed sensor it can be assumed that there is no coupling between reference measurement and the secondary path. First the algorithms are tested in simulation, then they are implemented on a test rig for active vibration control of unbalance induced rotor vibration. In simulation as well as for the test rig the performances of the algorithms are compared to each other. Advantages and drawbacks of the algorithms are discussed and practical instructions for implementation are given. The work is intended to serve as starting point and motivation for future research in this field of study. [ABSTRACT FROM AUTHOR]

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method. [ABSTRACT FROM AUTHOR]