Residual life prediction of gear based on long term and short term memory network

Gear as the transmission device of mechanical equipment, its health has a great impact on the stability and life of the equipment. With the improvement of the integration of modern mechanical equipment, the accuracy of gear is also higher and higher. In the long-term operation, it is easy to wear, resulting in tooth fracture. Monitoring the health status of gear is an important means to ensure the reliable operation of mechanical equipment. Based on reliability and economy, gear fault prediction and health management (PHM) has been paid more and more attention, and the remaining useful life (rul) prediction is the core research content.

With the development of information sensing technology, the number of monitoring points and signal sampling frequency of the equipment increase, and the equipment can obtain a large amount of data in the process of operation, which promotes PHM into the era of big data. How to use massive data to mine the degradation rule of equipment state, so as to predict the remaining life of equipment, has become a big challenge for prediction and health management.

The gear is usually packaged in the gearbox, and the information received by the sensor reflects the degradation state of the equipment. The data-driven residual life prediction method is based on the theory of statistics and artificial intelligence. It uses sensors to obtain the data that characterizes the degradation of equipment status, so as to realize real-time residual life prediction. Liu Ying et al. Used time series ARMA (auto regressive moving average) prediction model to diagnose steam turbine faults according to analog signals, but ARMA prediction model is suitable for short-term prediction of time series and processing stable data with certain regularity. The first mock exam is to predict the faults of the gearbox by using WANG vector and machines regression (SVR) and X Support (spectrum). The results of the model are better than those of the single model.

But the structure of the model is complex, when the sample data is large, the prediction results are time-consuming. Davies TM and others used artificial neural network (ANN) to predict the faults of diesel engine turbocharger and automobile engine, and used genetic algorithm to optimize the network structure parameters of ANN. Neural network plays a key role in Ann. The complexity of calculation depends on the number of neurons and the number of hidden layers. If there are too many samples, it is easy to cause “dimension disaster” or “over learning problem”.