Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.

In order to meet the requirements of online condition monitoring and automatic fault diagnosis of plants, the leakage errors of spectra in discrete spectrum analysis are corrected automatically. Aiming at the leakage errors in spectra of signals windowed by rectangular window, a automatic correcting method of leakage errors is presented based on the extremum searching method(ESM) and the energy centrobaric correction method(ECCM). The steps of the method are: firstly, compute the Discrete Fourier Transform (DFT) of the signal by Fast Fourier Transform (FFT) method and get the amplitude spectrum of it; secondly, find the all points needed to be corrected by the extremum searching method; thirdly, correct the frequencies, amplitudes and phases of the corresponding points by energy centrobaric correction method; finally, get the corrected signal by Inverse Fast Fourier Transform (IFFT) method. The simulation results certify that the leakage errors of spectra after being corrected can be controlled within 3% when the minimal difference in neighbouring frequency components is greater than 10 times of the frequency resolution. This correcting method can meet the requirements of engineering practice.

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