A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 11; pp. 8792 - 8802
• Main Authors: Huang, Cheng-Geng, Huang, Hong-Zhong, Li, Yan-Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft engine; Aircraft engines; Artificial intelligence; Data models; data-driven prognostic; deep learning (DL); Engines; Feature extraction; long short-term memory; Methods; prognostic and health management; remaining useful life (RUL) estimation; Sensors; Testing; Training; Turbofan engines

Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develop...