Abstract
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.
Highlights
By the techniques of prediction, equipment status deterioration laws can be drawn out from the machine historical state parameters and potential faults can be predicted
The prediction methods of autoregressive forecast, grey theory, neural network, and so forth are the methods based on data [4,5,6, 13,14,15,16]
The time sequence vibration vector is used as input parameters and a data driven AR prediction model is built with fixed order
Summary
By the techniques of prediction, equipment status deterioration laws can be drawn out from the machine historical state parameters and potential faults can be predicted. Scholars have conducted lots of work on the aspect of equipment fault prediction, and many prediction methods are proposed [2,3,4,5,6]. These methods can be roughly divided into three categories: knowledge based, model based, and data based. The prediction method based on data has a wide range and low cost It is the most practical method and has become the research focus and development trend in the field of fault diagnosis and prediction. According to the theory of full spectrum and holographic and full vector spectrum [1, 17,18,19], the vibration track under a vibration harmonic is an ellipse
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