Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive or labor-intensive. Recently, Prognostics and Health Management has emerged as an optimal way to manage maintenance operations. In such a system, fault prognostics aims to predict the remaining useful life based on the sensor measurements. In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated. Real-world run-to-failure data of two independent fault-types in two different engine load profiles are collected from a hybrid power lab. The first profile is used for training and validation, while the second is used for testing. The LSTM networks are used to construct the fault prognostics model. Experiments and comparisons are performed to obtain the optimal structure of the networks. Results show that the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.

中文翻译：

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使用 LSTM 网络进行故障预测：在船用柴油机中的应用


维护保养是保证海洋船舶安全高效运行的关键。目前，反应性维护和预防性维护是船上使用的两种主要方法。这些方法要么是成本密集型的​​，要么是劳动力密集型的。最近，预测和健康管理已成为管理维护操作的最佳方式。在这样的系统中，故障预测旨在根据传感器测量结果预测剩余使用寿命。本文研究了将数据驱动的故障预测应用于船用柴油机的可行性。从混合动力实验室收集了两种不同发动机负载曲线中两种独立故障类型的实际运行故障数据。第一个配置文件用于训练和验证，第二个配置文件用于测试。 LSTM网络用于构建故障预测模型。进行实验和比较以获得网络的最佳结构。结果表明，所提出的方法可以很好地推广第二个轮廓，并提供高精度的剩余使用寿命预测。