  Survival analysis is playing a major role in manufacturing sector by
analyzing occurrence of any unwanted event based on the input data. Predictive
maintenance, which is a part of survival analysis, helps to find any device
failure based on the current incoming data from different sensor or any
equipment. Deep learning techniques were used to automate the predictive
maintenance problem to some extent, but they are not very helpful in predicting
the device failure for the input data which the algorithm had not learned.
Since neural network predicts the output based on previous learned input
features, it cannot perform well when there is more variation in input
features. Performance of the model is degraded with the occurrence of changes
in input data and finally the algorithm fails in predicting the device failure.
This problem can be solved by our proposed method where the algorithm can
predict the device failure more precisely than the existing deep learning
algorithms. The proposed solution involves implementation of Deep Reinforcement
Learning algorithm called Double Deep Q Network (DDQN) for classifying the
device failure based on the input features. The algorithm is capable of
learning different variation of the input feature and is robust in predicting
whether the device will fail or not based on the input data. The proposed DDQN
model is trained with limited or lesser amount of input data. The trained model
predicted larger amount of test data efficiently and performed well compared to
other deep learning and machine learning models.
