Development of a Model for Predicting the Residual Useful Life of Data Drives Using Recurrent Neural Network Technology
This article discusses the problem of predicting the remaining useful life, which often arises when servicing disk drives. Possibilities of effective solution of this problem using a regression model using recurrent neural networks, in particular, using LSTM (Long Short-Term Memory), are investigated. At the same time, to develop a forecasting model based on neural networks, data sets are used, which are placed in the public domain of the BlackBlaze service. The data is considered as multidimensional time series, which were formed based on the results of the readings of SMART (Self-Monitoring, Analysis and Reporting Technology) sensors of disk drives. Approaches to improving forecasting accuracy are investigated. Examples of predicting the remaining useful life of disk drives are given, as well as graphical dependences of the loss function and comparative tables obtained on the training and validation samples.