Citation: Zhang, P.; Cao, L.; Dong, F.;
Gao, Z.; Zou, Y.; Wang, K.; Zhang, Y.;
Sun, P. A Study of Hybrid Predictions
Based on the Synthesized Health
Indicator for Marine Systems and
Their Equipment Failure. Appl. Sci.
2022, 12, 3329. https://doi.org/
10.3390/app12073329
Academic Editor: José A. Orosa
Received: 16 February 2022
Accepted: 24 March 2022
Published: 25 March 2022
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Article
A Study of Hybrid Predictions Based on the Synthesized Health
Indicator for Marine Systems and Their Equipment Failure
Peng Zhang
1,2
, Lele Cao
1
, Fangyang Dong
1
, Zeyu Gao
1
, Yongjiu Zou
1
, Kai Wang
1
, Yuewen Zhang
1,2,
*
and Peiting Sun
1,
*
1
Marine Engineering College, Dalian Maritime University, Dalian 116026, China;
zhangpenglunji@dlmu.edu.cn (P.Z.); cll1996@dlmu.edu.cn (L.C.); dongfangyang@dlmu.edu.cn (F.D.);
gaozeyu659@dlmu.edu.cn (Z.G.); zouyj0421@dlmu.edu.cn (Y.Z.); kwang@dlmu.edu.cn (K.W.)
2
Collaborative Innovation Research Institute of Autonomous Ship, Dalian Maritime University,
Dalian 116026, China
* Correspondence: zhangyuewen@dlmu.edu.cn (Y.Z.); sunptg@dlmu.edu.cn (P.S.)
Abstract:
Ship mechanical system health prognosis is one of the major tasks of ship intelligent
operation and maintenance (O&M). However, current failure prediction methods are aimed at single
pieces of equipment, and system-level monitoring remains an underexplored area. To address this
issue, an integration method based on a synthesized health indicator (SHI) and dynamic hybrid
prediction is proposed. To accurately reflect the changes in system health conditions, a multi-state
parameter fusion method based on dynamic kernel principal component analysis (DKPCA) and
the stacked autoencoder (SAE) is presented, along with construction of a system SHI. Taking into
consideration that the system degradation process includes global degradation trends, local self-
healing phenomena, and local interference, a dynamic hybrid prediction model is established after
SHI decomposition. The performance of the proposed approach is applied to a ship fuel-oil system to
show its effectiveness.
Keywords:
operation and maintenance; synthesized health indicator; degradation process;
failure prognosis
1. Introduction
The newest generation of artificial intelligence technology has promoted the process
of ship autonomy and unmanned operation. Ship mechanical systems should make full
use of new technology to achieve scientific O&M based on improving safety, reliability, and
efficiency [
1
,
2
]. The main research focus of ship O&M includes all-round state perception,
real-time condition monitoring and evaluation, health condition prognostics, independent
decision-making [3], and other technologies.
More and more academic researchers and O&M engineers have been involved in
machinery failure prognostics. For single-component equipment, some research has shown
that incorporating prognostic information helps make more reasonable O&M decisions [
4
,
5
].
Modern merchant vessels are complex systems constructed from numerous subsystems,
with equipment and components provided by multiple different suppliers and integrated
by a shipyard. There are often one or more types of economic, structural, and stochastic
interactions between components. Intelligent O&M usually needs to consider the whole-
system health condition in order to optimize system-level and even plant-level decision
making [
6
–
8
]. The existence of these interdependencies makes the single-equipment prog-
nostic model no longer applicable.
At present, system-level failure prediction mainly includes two main problems, one is
to establish a suitable SHI that can accurately describe the health condition of the system, the
other is to select an appropriate prediction method according to the HI to realize the failure
prediction [
8
]. Ship systems are complex mechanical systems designed to complete specific
Appl. Sci. 2022, 12, 3329. https://doi.org/10.3390/app12073329 https://www.mdpi.com/journal/applsci
