Rotary machine residual life prediction method of multi-layer bidirectional gating cycle unit network

Abstract

The invention discloses a rotary machine residual life prediction method of a multi-layer bidirectional gating cycle unit network. The rotary machine residual life prediction method comprises the following steps: acquiring a vibration signal; constructing health indexes; constructing a network training set; constructing a multi-layer bidirectional gating circulation unit network; training a multi-layer bidirectional gating cycle unit network; and carrying out network testing, residual life estimation and confidence interval acquisition; and performing residual life prediction evaluation. According to the rotary machine residual life prediction method, the advantages of strong feature extraction capability of deep learning are combined, and regression prediction is carried out by utilizinga bidirectional gated cycle unit neural network, and the confidence interval of the residual life is obtained through a Bootstrap method. Aiming at the problems that the model precision is sensitive to the value of the learning rate in the training process of a recurrent neural network model, the prediction performance of the model is influenced by too high and too low values, and the neural network is efficiently trained by utilizing the natural exponential decay learning efficiency. The rotary machine residual life prediction method can accurately predict the residual life and confidence interval of the rotary machine, can greatly reduce the expensive unplanned maintenance, and avoids the occurrence of a big disaster.

Description

technical field [0001] The invention relates to a technology for predicting the remaining life of a rotating machine, in particular to a method for predicting the remaining life of a rotating machine based on a multi-layer bidirectional gated cycle unit network. Background technique [0002] Due to the development of advanced sensor and computer technology, a large amount of condition monitoring data has been accumulated in industrial production, and data-driven methods have been widely used in the prediction of mechanical equipment remaining life, because they can use condition monitoring data to quantify the degradation process, while Rather than building an accurate system model that is not readily available. [0003] Fault prediction and health management includes four stages: fault detection, fault diagnosis, remaining life prediction and health management. When a fault is discovered or diagnosed, the machine is usually shut down as quickly as possible to avoid catastr...

AI Technical Summary

Problems solved by technology

Predictions often vary widely due to measurement noise and model parameters

Only point estimation of remaining life cannot meet the actual requirements

In order to express the uncertainty of the prediction results, it is necessary not only to calculate the determined predicted value of remaining life, but also to calculate the confidence interval of remaining life; (2) The remaining life prediction with deep learning method is to use fixed learning efficiency to train the network, which is inefficient (3) The model-based method tries to establish a mathematical or physical model to describe the degradation process of the machinery, and uses the measured data to update the model parameters. In practice, it is difficult to find an accurate model to describe the degradation process of the rotating machinery

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