New intelligent hybrid prediction model for condition trend of electromechanical equipment

Due to the fluctuation and complexity of electromechanical equipment operation condition affected by various factors, it is difficult to use a single prediction method to accurately describe its moving trend. So a new hybrid prediction model based on improved grey system, support vector machine (SVM) and neuro-fuzzy system is proposed. In this model, the fluctuation of the data sequence is weakened by the improved grey system, the SVM can deal with small samples and neuro-fuzzy system is capable of processing non-linear fuzzy information. The hybrid prediction model combines these advantages and its prediction result is an adaptive combination of these single method's via improved genetic algorithms. This model was applied to the trend prediction of a fluctuant and complicated benchmark data and a vibration trend signal from machine sets. Testing results show that the prediction performance of this model outperforms any one of the three prediction methods.