Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes

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EXTREME LEARNING MACHINE AND DEEP LEARNING NETWORKS

Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes Jian-Ming Bai1,2 • Guang-She Zhao3 • Hai-Jun Rong1 Received: 1 January 2019 / Accepted: 29 August 2019 Ó Springer-Verlag London Ltd., part of Springer Nature 2019

Abstract This paper aims to improve data-driven prognostics by presenting a novel approach of directly estimating the remaining useful life (RUL) of aero-engines without requiring setting any failure threshold information or estimating degradation states. Specifically, based on the sensory data, RUL estimations are directly obtained through the universal function approximation capability of the extreme learning machine (ELM) algorithm. To achieve this, the features related with the RUL are first extracted from the sensory data as the inputs of the ELM model. Besides, to optimize the number of observed sensors, three evaluation metrics of correlation, monotonicity and robustness are defined and combined to automatically select the most relevant sensor values for more effective and efficient remaining useful life predictions. The validity and superiority of the proposed approach is evaluated by the widely used turbofan engine datasets from NASA Ames prognostics data repository. The proposed approach shows improved RUL estimation applicability at any time instant of the degradation process without determining the failure thresholds. This also simplifies the RUL estimation procedure. Moreover, the random properties of hidden nodes in the ELM learning mechanisms ensures the simplification and efficiency for real-time implementation. Therefore, the proposed approach suits to real-world applications in which prognostics estimations are required to be fast. Keywords Remaining useful life (RUL)  Aero-engines  Extreme learning machine (ELM)

1 Introduction Prognostics and health management (PHM) have the ability of improving the quality and reliability of aircrafts by means of the health state assessment and the remaining life estimation of a particular system or component (aero& Hai-Jun Rong [email protected] 1

State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China

2

Optical Direction and Pointing Technique Research Department, Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, People’s Republic of China

3

School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China

engine, bearing, actuator, etc). Thus, it has become one of the most important condition-based maintenance (CBM) activities in the aviation industries [7, 19, 30] and enables intelligent decision making for life-critical and missioncritical applications [1, 4, 6, 11, 17, 26, 50]. Earlier before 2008, the Prognostics Center of Excellence (CoE)