Distributed Training for Data Driven Models in Power Machinery Online Monitoring

Abstract

Abstract Prognostics and Health Management (PHM) Systems have been widely applied in online monitoring of modern mechanical devices, and models in PHM systems are of vital importance. Traditional mechanism models are able to simulate parameters and evaluate the overall machine state accurately, but their defect is the high requirements in understanding the objects. Therefore, data driven models could be a suitable substitute. Data driven models take the advantage of machine learning technique and are able to be established only on the basis of past data. Models applied in this paper are based on Artificial Neural Network (ANN), but the complexity of network structures will occupy huge amount of computing resources and time in training. For online monitoring, spending too much time on training will postpone real time prediction and decrease the reaction speed of PHM systems in abnormal conditions. In this paper distributed training based on computer cluster is proposed to decrease training time. By dispatching computing task into several workers, the severs in the cluster will only undertake a small fraction of the total computing load and therefore accelerate the overall training process. On the other hand, there are some risks of losing prediction accuracy and stability because of the nonlinear gradient deviation in data parallelism. Aggregation period (AP) is an important factor in balancing the requirements of both ends. This paper analyzes the influence of AP on training speed, accuracy and stability, then proposes a novel distributed training algorism according to the regulations achieved. Then a distributed training and online monitoring process for a typical two-shaft gas turbine is taken as an example in the result and discussion section. It turns out that the experimental results fit the theoretical regulations well, and the revised distributed learning algorism is able to meet all the requirements of training speed, prediction accuracy and stability.