Citation: Zhao, J.; Gao, C.; Tang, T.;
Xiao, X.; Luo, M.; Yuan, B. Overview
of Equipment Health State
Estimation and Remaining Life
Prediction Methods. Machines 2022,
10, 422. https://doi.org/10.3390/
machines10060422
Academic Editor: Ahmed
Abu-Siada
Received: 23 April 2022
Accepted: 25 May 2022
Published: 26 May 2022
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Review
Overview of Equipment Health State Estimation and
Remaining Life Prediction Methods
Jingyi Zhao
1,2,
* , Chunhai Gao
2
, Tao Tang
1
, Xiao Xiao
2
, Ming Luo
2
and Binbin Yuan
2
1
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
ttang@bjtu.edu.cn
2
Traffic Control Technology Co., Ltd., Beijing 100070, China; chunhai.gao@bj-tct.com (C.G.);
xiao.xiao@bj-tct.com (X.X.); ming.luo@bj-tct.com (M.L.); binbin.yuan@bj-tct.com (B.Y.)
* Correspondence: netzhaojy@163.com or jingyi.zhao@bj-tct.com; Tel.: +86-010-8360-6057
Abstract:
Health state estimation can quantitatively evaluate the current degradation state of equip-
ment, and remaining life prediction can quantitatively predict the remaining service time of equip-
ment. These two technologies can provide a basis for condition-based maintenance and predictive
maintenance of equipment, respectively. In recent years, a large amount of research has been imple-
mented in these two technologies. However, there is not any systematic review that covers these two
technologies, and their engineering applications, comprehensively. To fill the gap, this paper makes
a comparative analysis of existing health state estimation and remaining life prediction methods,
and details the characteristics and limitations of various methods. The engineering applications of
these two methods are summarized, and their applicable objects are briefly given. Finally, these two
methods are summarized, and their feasibility for engineering application is discussed. This work
provides guidance for the selection of industrial equipment health assessment and remaining life
prediction methods.
Keywords: health state estimation; remaining life prediction; health state; condition assessment
1. Introduction
In the past decades, with increasing equipment complexity and integration, the failure
rate has gradually increased. In order to ensure equipment’s smooth completion of various
tasks and reduce the maintenance cost in the life cycle, prognostics and health management
(PHM) technology was born in the 1970s [
1
]. PHM technology represents a change in
concept, which enables the maintenance and management of equipment to engage in
post-treatment and passive maintenance, regular inspection, active protection, and then
to advance prediction and comprehensive management [
2
]. This technology has been
intensively studied and widely used in the UK, USA and other countries. It is an important
part of equipment maintenance and management. Health state estimation and remaining
life prediction are key technologies in PHM [
3
]. Health state estimation and remaining life
prediction mainly collect the output data of the equipment through various sensors, process
and analyze the data with the help of various algorithms, comprehensively evaluate the
health of the equipment and predict the remaining service time of the equipment [
4
]. With
the help of these two technologies, the degradation trend of the equipment can be identified
and the future service time can be evaluated. Furthermore, maintenance management
opinions can be provided in time, so as to improve the reliability and supportability of
the equipment.
There are three main ways to evaluate the health state equipment, as shown in Table 1.
The first two involve evaluating the health state level of the equipment, and the third one
is to evaluate the health value of the equipment [
5
]. Initially, engineers only used fault and
normal binary functions to judge the health state of equipment [
6
]. This method is relatively
mature and insufficient to define the state of equipment only by binary functions. Later,
Machines 2022, 10, 422. https://doi.org/10.3390/machines10060422 https://www.mdpi.com/journal/machines
