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Ship mechanical system health prognosis is one of the major tasks of ship intelligent operation and maintenance (O&amp;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.

A Study of Hybrid Predictions Based on the Synthesized Health Indicator for Marine Systems and Their Equipment Failure