基于模糊逻辑的船舶推进系统可解释预测性维护
Explainable Predictive Condition-based Maintenance of Naval-Propulsion Systems using Fuzzy Logic
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中文总结 AI 辅助
本文提出一种结合模糊决策树与深度残差神经网络的框架，用于船舶推进系统的可解释预测性维护，在公开数据集上达到99.24%的准确率。
中文摘要 AI 辅助
航运业对全球经济具有重大影响，强调通过有效的维护技术来确保运营可用性和安全性的必要性。在过去几十年中，预测性维护（PdM）相比现有的传统维护系统已成为一种有前景的解决方案。这是因为它提供了若干有利功能，例如船舶部件的损伤预测、减少停机时间、改善和延长机械寿命，以及航行期间更高的安全性。然而，现有的用于执行PdM的方法并未向用户解释其结果，以便他们能够理解可能发生的故障。为解决这一局限，本文提出了一种基于模糊决策树和深度残差神经网络的新框架，旨在对海军舰艇执行可解释的预测性维护。所提出的框架能够基于所使用的数据集生成模糊局部规则，并利用因果关系以用户可理解的方式提供其结果的解释，从而获得用户的信任。使用公开数据集的实验证明了该框架的有效性，其准确率达到99.24%。
英文摘要
The shipping industry has a significant impact on the global economy, emphasizing the need for operational availability and safety through the use of effective maintenance techniques. During the last decades, predictive maintenance (PdM) has emerged as a promising solution compared to the existing conventional maintenance systems. This is because it offers several advantageous functions, such as damage predictions for vessel components, reduced downtime, improved and extended life of machinery, as well as higher safety during voyages. However, existing methodologies developed for performing PdM do not provide explanations of their results to users, so that they can understand the failures that may occur. To address this limitation, this paper proposes a novel framework based on a fuzzy decision tree and a deep residual neural network, aiming to perform explainable PdM on naval vessels. The proposed framework is able to generate fuzzy local rules based on the dataset used, and can provide explanations of its outcomes, using cause-and-effect relationships, in a way that are understandable to users, thereby gaining their trust. Experiments using a publicly available dataset demonstrate the effectiveness of the proposed framework, as it achieves an accuracy of 99.24%.
发表机构
- University of Thessaly（塞萨利大学）
机构由 AI 辅助整理，请以论文原文为准。
