Uncertainty-based combination of signal processing techniques for the identification of rotor imbalance

This paper describes a method for the uncertainty-based combination of signal processing techniques for the identification of rotor imbalance. The main idea of the proposed method is to compute the imbalance with different algorithms and to average the different algorithms' results. The method is based on the data fusion at feature level and uses the measurement uncertainty of the imbalance as a figure of merit for the weight computation. A static, a dynamic, and a hybrid implementation are presented. In the static one, the weights are computed in a dedicated training phase, in which four algorithms (Fourier transform and quasi-harmonic fitting of signal denoised with Hilbert-Huang Transform, Hilbert Vibration decomposition, and Wavelet Packet decomposition) have been used to estimate the known imbalance of car wheels. In the dynamic one, the weights are computed at runtime by estimating the difference between each predictor and the actual signal. The hybrid approach is the combination of the two algorithms. Results of simulations and experiments evidenced the validity of the data fusion, with uncertainty reductions between 10 and 40%, with larger benefits in presence of non-stationary disturbances.