Purpose

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

Design/methodology/approach

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

Findings

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

Research limitations/implications

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

Practical implications

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

Originality/value

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

中文翻译：

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基于相似性信息融合的航空发动机剩余使用寿命预测灰色模型

目的

由于传感和计算技术的快速发展，大型复杂设备的在线健康监测已成为设备诊断和预测领域的趋势。本文的目的是构建一个基于相似信息融合的更准确、更稳定的灰色模型来预测飞机发动机的实时剩余使用寿命（RUL）。

设计/方法/方法

首先，通过对历史样本应用多元线性回归来创建参考数据库。然后在被监控的引擎和历史样本之间进行相似性匹配。之后，结合监测传感数据的最新趋势和多个相似参考样本的长期趋势，应用信息融合灰色模型预测监测发动机未来的退化轨迹，并得到相应的实时RUL。

发现

比较分析结果表明，所提出的模型称为基于相似性的信息融合灰色模型（SIFGM），可以从早期退化阶段提供更好的 RUL 预测。此外，当仅参考样本的部分信息可用时，SIFGM 仍然能够相对准确地预测系统故障，这使得该方法在历史全生命周期数据稀缺时成为可行的选择。

研究限制/影响

SIFGM 方法的预测基于从监测感官信号合成的单个单调变化的健康指标 (HI)，假设其与发动机的退化过程高度相关。

实际影响

SIFGM 可用于预测那些在故障发生前具有类似不可逆退化行为的在线状态监测系统的退化轨迹和 RUL，例如飞机发动机和离心泵。

原创性/价值

本文将相似性信息引入到传统的GM(1,1)模型中，使其更适合长期RUL预测，同时也为有限的历史全生命周期数据提供了基于相似性的RUL预测解决方案。