Attention-based deep survival model for time series data

In the era of internet of things and Industry 4.0, smart products and manufacturing systems emit signals tracking their operating condition in real-time. Survival analysis shows its strength in modeling such signals to determine the condition of in-service equipment and products to yield critical operational decisions, i.e., maintenance and repair. One appealing aspect of survival analysis is the possibility to include subjects in the model which did not have their failure yet or when the exact failure time is unknown. NN-based survival models, i.e., deep survival models, show superior performance in modeling the nonlinear relationship between the reliability function and covariates. We propose a novel deep survival model, seq2surv, to incorporate the seq2seq structure and attention mechanism to enhance the ability to analyze a sequence of signals in the survival analysis. Similar to the seq2seq model which shows superior performance in machine translation, we designed the seq2surv model to translate from a sequence of signals to a sequence of survival probabilities and to update the reliability predictions along with real-time monitoring. Our results show that the seq2surv model outperforms existing deep survival approaches in terms of higher prediction accuracy and lower errors in the survival function estimation on both simulated and real-world datasets.