Remaining Useful Life Estimation of Complex Components in an Operational Environment
A Deep Learning Approach
J.A. Croese (TU Delft - Aerospace Engineering)
M.A. Mitici (Air Transport & Operations)
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Abstract
Over the recent years a significant amount of research has been conducted to develop models which are able to estimate a components Remaining Useful Life (RUL) based on available sensor readings. In this research a deep learning (DL) model in combination with a similarity-based curve matching technique is used to estimate the RUL of a component. The data-driven RUL estimation scheme consist of an online and offline step. In the online step, a 1-dimensional Convolutional Auto-Encoder (1DConvNet-AE) is trained in an unsupervised way to convert multi-sensor readings, collected from historical run-to-failure instances, into a 1-dimensional reconstruction error vector. The set of generated 1-dimensional reconstruction error vectors is used to generate 1-dimensional Health Index (HI) curves which represent the various degradation paths of the run-to-failure instances. The HI curves based on historical run-to-failure instances are stored in the HI library. In the online step, multi-sensor readings of a test instance are converted into an online HI curve, representing the degradation path of an operational component. By using the similarity-based curve matching technique, the online generated HI curve is matched with the HI curves situated in the HI library. The RUL is estimated based on a weighted average of a set of matches which pass a set similarity threshold value. The proposed procedure is tested on an operational dataset. This research shows the existence of a relation between the increase of reconstruction error and health deterioration over time. The RUL estimation performance is considered in an operational evaluation procedure in which a similarity threshold value is included. This research shows a usable RUL can be estimated directly from raw multi-sensor input data by the proposed model.