Bidirectional handshaking LSTM for remaining useful life prediction

Unpredictable failures and unscheduled maintenance of physical systems increases production resources, produces more harmful waste for the environment, and increases system life cycle costs. Efficient remaining useful life (RUL) estimation can alleviate such an issue. The RUL is predicted by making use of the data collected from several types of sensors that continuously record different indicators about a working asset, such as vibration intensity or exerted pressure. This type of continuous monitoring data is sequential in time, as it is collected at a certain rate from the sensors during the asset's work. Long Short-Term Memory (LSTM) neural network models have been demonstrated to be efficient throughout the literature when dealing with sequential data because of their ability to retain a lot of information over time about previous states of the system. This paper proposes using a new LSTM architecture for predicting the RUL when given short sequences of monitored observations with random initial wear. By using LSTM, this paper proposes a new objective function that is suitable for the RUL estimation problem, as well as a new target generation approach for training LSTM networks, which requires making lesser assumptions about the actual degradation of the system.