Ruochen Yang

Remaining useful life (RUL) serves as a key indicator of system health, and its accurate and timely prediction supports informed decision-making for efficient operation and maintenance. This is essential for complex engineering systems (CESes) such as unmanned surface vessels (USVs), where the human operators have limited opportunity to intervene during the operation. This paper proposes a framework for predicting the RUL of the CESes. The proposed framework employs a probabilistic deep learning (PDL) approach to predict the component's RUL and an equation node-based Bayesian network (BN) to predict system RUL (SRUL) at any future time-step. The component-level RUL method is validated using the NASA's Commercial Modular Aero-Propulsion System Simulation (c-mapss) dataset, and then the proposed framework is demonstrated with a USV case study. The results are evaluated using a set of quality metrics. By making use of the condition-monitoring sensor data, component reliability data, and models that account for the complex causal relationships between components, the proposed framework can provide near real-time predictions of the RUL with uncertainty of a CES, thus supporting its informed decision-making during the operation.

With the increasing popularity and deployment of unmanned surface vessels (USVs) all over the world, prognostics and health management (PHM) has become an indispensable tool for health monitoring, fault diagnosis, health prognosis, and maintenance of marine equipment on USVs. USVs are designed to undertake critical and extended missions, often in extreme conditions, without human intervention. This makes the USVs susceptible to equipment malfunction, which increases the probability of system failure during mission execution. In fact, in the absence of any crew onboard, system failure during a mission can create a great inconvenience for the concerned stakeholders, which compels them to design highly reliable USVs that must have integrated intelligent PHM systems onboard. To improve mission reliability and health management of USVs, researchers have been investigating and proposing PHM-based tools or frameworks that are claimed to operate in real time. This paper presents a comprehensive review of the existing literature on recent developments in PHM-related studies in the context of USVs. It covers a broad perspective of PHM on USVs, including system simulation, sensor data, data assimilation, data fusion, advancements in diagnosis and prognosis studies, and health management. After reviewing the literature, this study summarizes the lessons learned, identifies current gaps, and proposes a new system-level framework for developing a hybrid (offline–online) optimization-based PHM system for USVs in order to overcome some of the existing challenges.