Distributed energy power stations are developing rapidly because of their cleanliness

environmental protection

economy and high efficiency. However

there are few data used for fault diagnosis of plant equipment

so a method to predict the health state and aging degree of equipment is urgently needed. Based on this

a prediction model which can analyze the running state of equipment and obtain the deterioration trend of equipment is proposed.Firstly

multi-dimensional data of the equipment is preprocessed

and an improved Mahalanobis distance based equipment health model of distributed energy power station is constructed quantitatively by combining the analytic hierarchy process(AHP) with Gaussian mixture distribution. Then

the combined prediction model based on the improved sparrow algorithm and short and long time memory neural network is established to predict the trend and correlation analysis of the deterioration of distributed energy power plant equipment. The experimental results show that the proposed fusion health model can predict equipment anomalies in the case of insufficient actual fault data of distributed energy power stations.