Fault prediction algorithm of slow variable signal based on multidimensional data driving

With the increase of fault history data, the problem of high precision and long-time fault prediction under different failure modes is presented. We propose a multi-channel fusion fault prediction algorithm based on Long Short-Term Memory (LSTM) deep network. The prediction ability of the algorithm increases with the increase of training samples. Based on the analysis of the influence of different network parameters on the prediction accuracy, the optimal network parameters are selected to realize the long-time and high-precision fault prediction. It can recognize fault prediction without historical data. And it can integrate multi-channel information for off-line training to achieve the goal of self-increasing fault prediction ability.