Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece.
Department of Mechanical Engineering and Aeronautic, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2021 Aug 23;21(16):5658. doi: 10.3390/s21165658.
The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations at the core of system design. Used wisely, data can help the shipping sector to achieve operating cost savings and efficiency increase, higher safety, wellness of crew rates, and enhanced environmental protection and security of assets. The main goal of this study is to develop a methodology able to harmonize data collected from various sensors onboard and to implement a scalable and responsible artificial intelligence framework, to recognize patterns that indicate early signs of defective behavior in the operational state of the vessel. Specifically, the methodology examined in the present study is based on a 1D Convolutional Neural Network (CNN) being fed time series directly from the available dataset. For this endeavor, the dataset undergoes a preprocessing procedure. Aspiring to determine the effect of the parameters composing the networks and the values that ensure the best performance, a parametric inquiry is presented, determining the impact of the input period and the degree of degradation that our models identify adequately. The results provide an insightful picture of the applicability of 1D-CNN models in performing condition monitoring in ships, which is not thoroughly examined in the maritime sector for condition monitoring. The data modeling along with the development of the neural networks was undertaken with the Python programming language.
利用数据获取有用和可操作的信息并提供见解的能力是持续流程改进的基本要素。海洋工程将数据视为资产，将数据考虑置于系统设计的核心。明智地使用数据可以帮助航运部门节省运营成本和提高效率，提高船员的安全和健康水平，增强环境保护和资产安全。本研究的主要目标是开发一种能够协调从船上各种传感器收集的数据的方法，并实施一个可扩展和负责任的人工智能框架，以识别表明船舶运行状态下出现缺陷行为的早期迹象的模式。具体来说，本研究中检查的方法基于一维卷积神经网络（CNN），直接从可用数据集提供时间序列。为此，数据集需要经过预处理过程。为了确定构成网络的参数和确保最佳性能的值的影响，提出了参数查询，确定了输入周期和我们的模型能够充分识别的退化程度的影响。结果提供了一个有见地的图片，说明一维卷积神经网络模型在船舶进行状态监测中的适用性，这在海上领域的状态监测中并没有得到深入研究。数据建模以及神经网络的开发都是使用 Python 编程语言完成的。
