Remaining useful life estimation using Long Short-term Memory (LSTM) neural networks and deep fusion

Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health Management (PHM) for condition-based maintenance of machinery. In order to transmit and store the sensor data for archiving and long term analysis, data compression techniques are regularly utilized to reduce the requirements of bandwidth and storage in modern remote PHM systems. In these systems the challenge arises of how the compressed sensor data affects the RUL estimation algorithms. Conventional data driven approaches for RUL estimation use statistical modeling of raw sensor data and operational data to estimate RUL. A main drawback of these methods is that they require extensive expert prior knowledge and a significant number of assumptions. Alternative regression based approaches and deep neural networks, such as Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNNs) are known to have issues when modeling long-term dependencies in the sequential data. Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed (distorted) data. The proposed technique can explore and exploit the long-term dependencies of sensor sequence information in conjunction with the proposed information fusion method to estimate RUL without any prior knowledge or assumptions. The experimental results indicate that the proposed method is able to estimate RUL reliably with narrower error bands compared to other state-of-the-art LSTM approaches. Moreover, the proposed method is able to produce accurate RUL predictions from both the raw and compressed datasets with comparable accuracy