Remaining Useful Life Estimation of Aero-Engines with Self-Joint Prediction of Continuous and Discrete States

by   Rong-Jing Bao, et al.

The remaining useful life (RUL) estimation generally suffer from this problem of lacking prior knowledge to predefine the exact failure thresholds for machinery operating in dynamic environments. In this case, dynamic thresholds depicted by discrete states are effective to estimate the RUL of dynamic machinery. Currently, only few work considers the dynamic thresholds, and adopt different algorithms to predict the continuous and discrete states separately, which largely increases the complexity of the learning process. In this paper, we propose a novel prognostics approach for RUL estimation of aero-engines with self-joint prediction of continuous and discrete states within one learning framework. With modeling capability of self-adapting structure and parameters online, the quantized kernel recursive least squares (QKRLS) algorithm is introduced to predict the degrading states and also determine the discrete states with the kernel centers. The self-evolving dynamic kernel centers obtained during building predictors are automatically assigned as the discrete states for different engines without predefining them. Then, the RUL is estimated conveniently once the predicted degrading signals fall into the final fault state based on a distance metric. Finally, the results from turbofan engine datasets demonstrate the superiority of the proposed approach compared to other popular approaches.


page 1

page 2

page 3

page 4


Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

In the era of industrial big data, prognostics and health management is ...

Joint State and Input Estimation of Agent Based on Recursive Kalman Filter Given Prior Knowledge

Modern autonomous systems are purposed for many challenging scenarios, w...

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Predicting the remaining useful life of machinery, infrastructure, or ot...

Health Assessment and Prognostics Based on Higher Order Hidden Semi-Markov Models

This paper presents a new and flexible prognostics framework based on a ...

Stochastic Gradient Based Extreme Learning Machines For Online Learning of Advanced Combustion Engines

In this article, a stochastic gradient based online learning algorithm f...

Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction

Effective Prognostics and Health Management (PHM) relies on accurate pre...

eBPF-based Working Set Size Estimation in Memory Management

Working set size estimation (WSS) is of great significance to improve th...

Please sign up or login with your details

Forgot password? Click here to reset