Fault prognosis of Engineered Systems: A Deep Learning Perspective

In modern industry, engineered systems are generally required to work under complex operational conditions to complete specific missions. But most of existing data-driven prognostic methods still lack an effective model that can utilize operational conditions data to predict remaining useful life (RUL) of engineered systems. To fill these practical gaps, this paper develops a novel prognostic method based on Bi-Directional Long Short-Term Memory (BLSTM) network. The proposed method can effectively integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. In the proposed prognostic framework, multiple raw sensors data, operational conditions data, and inspection time epoch are preprocessed to form the desired sequences data with fixed length, and then are taken as main inputs and auxiliary inputs to fed into proposed BLSTM based network. And the labeled RUL are used as actual output for training the proposed model. Different from other deep learning (DL) based prognostic methods, the developed method can simultaneously model both sensors data and operational conditions data in a consolidated framework. The proposed approach is validated through a case study on aircraft turbofan engine, and comparisons with other existing state-of-the-art methods are also included.